# Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in
Generative AI

Jocelyn Dzuong<EMAIL_ADDRESS>Florida International UniversityMiami, FLUS ,
Zichong Wang<EMAIL_ADDRESS>Florida International UniversityMiami, FLUS and
Wenbin Zhang<EMAIL_ADDRESS>Florida International UniversityMiami, FLUS

(2024)

###### Abstract.

In the rapidly evolving landscape of generative artificial intelligence (AI),
the increasingly pertinent issue of copyright infringement arises as AI
advances to generate content from scraped copyrighted data, prompting
questions about ownership and protection that impact professionals across
various careers. With this in mind, this survey provides an extensive
examination of copyright infringement as it pertains to generative AI, aiming
to stay abreast of the latest developments and open problems. Specifically, it
will first outline methods of detecting copyright infringement in mediums such
as text, image, and video. Next, it will delve an exploration of existing
techniques aimed at safeguarding copyrighted works from generative models.
Furthermore, this survey will discuss resources and tools for users to
evaluate copyright violations. Finally, insights into ongoing regulations and
proposals for AI will be explored and compared. Through combining these
disciplines, the implications of AI-driven content and copyright are
thoroughly illustrated and brought into question.

Generative AI, copyright infringement, legal implications, copyright law,
digital art, watermarking, AI detection

††copyright: acmcopyright††journalyear: 2024

##  1\. Introduction

Figure 1. Actual screenshot from Dune (2021) versus its Midjourney-generated
counterpart

In the swiftly progressing realm of generative artificial intelligence (AI),
the pressing concern of copyright infringement emerges prominently. As AI
technologies continue to autonomously generate content from copyrighted data,
inquiries about ownership and safeguarding rights surface, reverberating
across diverse professional domains. This escalating trend raises critical
discussions surrounding ethical, legal, and socio-economic implications,
necessitating nuanced exploration and strategic interventions to navigate this
evolving landscape effectively. For instance, in July 2023 a group of
novelists collectively sued OpenAI for alleged usage of their books to train
their models and output similar content to the novelists’ prose (Small, 2023).
Moreover, in December 2023 The New York Times filed a lawsuit against OpenAI
and Microsoft, alleging copyright infringement by having its articles scraped
without permission to train their generative models (Stempel, 2023). More
recently, Marcus and Southen revealed how generative models such as Midjourney
and OpenAI’s Chat GPT-4 produced outputs strongly reminiscent of scenes from
copyrighted films and shows (Marcus and Southen, 2024; Thompson, 2024). As a
concrete example, Figure 1 illustrates how a prompt from Southen resulted in
an output resembling a shot from the trailer of Dune (2021). Notably,
Midjourney’s terms of service (Midjourney, 2023) highlight that users assume
liability when requesting the model to generate content featuring copyrighted
trademarks. This delegation of responsibility not only places the burden of
infringement on users, but also diverts accountability from Midjourney’s
developers, who have openly admitted to using copyrighted trademarks without
authorization (Salkowitz, 2022).

In light of these developments, this survey aims to delve into the complex
interplay between generative AI and protecting intellectual property (IP).
Through synthesizing existing methods and legal analyses, we provide a
comprehensive overview of the current landscape surrounding copyright in
generative AI. To the best of our knowledge, this work presents the first
thorough study on robust and applicable solutions to copyright issues in
generative AI, which also combines contextual legal analysis for future
consideration. The challenges and opportunities inherent in this burgeoning
field offer insights that can inform policymakers, practitioners, and
researchers alike when developing generative AI. Our main contributions are:
i) A detailed examination of the most advanced methods for detecting AI-
generated copyright violations across various mediums such as text, image, and
video, establishing itself as an invaluable resource for both researchers and
practitioners in the field. ii) Innovative strategies designed to safeguard
copyrights within the AI sphere, highlighting cutting-edge techniques like
watermarking, fingerprinting, and machine unlearning, contributing to the
protection of IP. iii) A comprehensive array of tools and resources for
assessing copyright violations, including extensive datasets, search engine
capabilities, and metrics quantifying infringement. iv) An in-depth analysis
of the regulatory framework surrounding generative AI, navigating through
current international copyright laws and proposing solutions to tackle the
emerging challenges in generative AI.

##  2\. Methods for Detecting AI-Generated Copyright Violations

Amidst the ongoing debate on copyright infringement facilitated by generative
AI, there has been sustained effort to devise effective detection methods.
This section walks through these approaches, focusing particularly on creative
mediums such as text, image, and video.

###  2.1. Text-based Infringement

Text-based infringement occurs when textual content is reproduced,
distributed, or communicated to the public without the consent of the
copyright holder. This issue has prompted researchers to develop innovative
solutions for identifying unlawful use of copyrighted text.

For instance, Li et al. introduce a framework called Digger, where passages of
consistent length are evaluated along with loss distribution from language
learning models (LLMs) to determine copyright infringement (Li et al., 2024a).
Figure 2 demonstrates the process of training on the textual datasets,
predicting instances of the dataset appearing in variants of GPT-2. Using a
loss function defined by $L(\Theta)=-\sum_{i}\log
P(u_{i}|u_{1},u_{2},...,u_{i-1};\Theta)$, where $u_{i}$ represents the i-th
token in a prompt, and $P(u_{i}|u_{1},u_{2},...,u_{i-1};\Theta)$ is the
probability of predicting token $u_{i}$, the evaluation of Area-Under-The-
Curve (AUC) values for each GPT-2 variant showed the XL model’s superior
efficacy in detecting copyright infringement, with a value of 0.9995 per 100
tokens.

Figure 2. The preparation, simulation, and confidence calculation phases of the Digger framework to determine whether an LLM is trained on certain protected datasets. Table 1. Recall and precision from the OpenWebText trained GPT-2 model when prompted with the PanDataset and GptPlagiarismDataset. Scores | PanDataset | GptPlagiarismDataset
---|---|---
|  Verbatim |  Paraphrase |  Idea |  Verbatim |  Paraphrase |  Idea
Precision |  0.995 |  1.00 |  1.00 |  0.96 |  0.846 |  0.99
Recall |  0.986 |  0.723 |  0.412 |  0.87 |  0.785 |  0.3

Similarly, Lee et al. devised an experiment based on the
OpenWebText111https://skylion007.github.io/OpenWebTextCorpus/ dataset using
variants of GPT-2 to investigate whether pre-trained and fine-tuned LLMs
plagiarize their training data (Lee et al., 2023). In the experiments,
passages were extracted from copyrighted texts to use as prompts for GPT-2.
These prompts were then provided to the LLMs, which generated text based on
the given input. Using cosine similarity (Lahitani et al., 2016) to assess the
likeness between generated text and original copyrighted passages, Lee et al.
reveal that GPT-2 adeptly reutilizes words, phrases, and fundamental concepts
from the texts it generates. Table 1 demonstrates the precision and recall of
GPT-2 for datasets from both OpenWebText and the PanDataset. With this they
demonstrate that this tendency is likely to exacerbate as the model size
increases and as more decoding algorithms are employed.

Although both strategies provide key perspectives on identifying copyright
infringement in generated text by adopting distinct methodologies, Li et al.
concentrate on AUC and loss distribution analysis to detect learned
copyrighted content. In contrast, Lee et al. assess direct similarity between
generated and original texts, including verbatim copying, paraphrasing, and
concept retention, making their approach potentially more pertinent and
efficient in pinpointing copyright violations. While further development of
robust detection methods is necessary, integrating these findings into future
methodologies can enhance their effectiveness.

###  2.2. Image-based Infringement

Image-based copyright infringement arises when visual content is copied,
shared, or disseminated without the authorization of the copyright owner. This
challenge has led to the creation of advanced technologies aimed at detecting
unauthorized reproduction and use of copyrighted images.

For instance, Trappey et al. present a robust trademark similarity assessment
system, in which they train a convolutional neural network (CNN) on 100,000
positive image pairs and 150,000 negative image pairs, setting aside 20,000
image pairs for validation (Trappey et al., 2019). As shown in Figure 3, they
train a Siamese neural network (SNN) (Koch et al., 2015) on these similarities
and extract feature vectors using a CNN pre-trained on the VGGnet architecture
(Simonyan and Zisserman, 2015). Upon evaluation using 3 single-scale CNNs, 3
multi-scale CNNs, and 1 multi-scale CNN with random filters, 2 of the multi-
scale CNNs with 3 layers + 7 layers and 3 layers + 5 layers was shown to be
the most accurate, with both having 76% training set accuracy and 89%
validation set accuracy.

Figure 3. Architectural flow of training a trademark detector using an SNN and
CNN on 250,000 image pairs.

Similarly, Kim et al. also train an SNN, combining regions of interest (RoI)
and image hashing to measure an input’s similarity to other existing images
and subsequently detect copyright infringement (Kim et al., 2021). By
combining methods of Image RoI, hashing, and verification, their proposed
method not only correctly identifies 88.7% of copyright infringement in
images, but also reduces the false positive rate from 91.8% to 2.8% when
compared to methods without image verification.

In comparing trademark similarity assessment by Trappey et al., which utilizes
multi-scale CNNs, against Kim et al.’s photo identification framework
combining ROI, hashing, and verification, it becomes evident that Kim et al.’s
approach offers a more refined solution. Not only does it ensure precise
identification of copyright breaches, but it also notably decreases the false
positive rate, demonstrating superior adaptability in pinpointing AI-generated
copyright infringement and holding a distinct advantage over Trappey et al.’s
method. By weaving the insights from these comparative analyses into the
fabric of future methodologies, we can significantly bolster our capacity to
combat copyright violations effectively.

###  2.3. Video-based Infringement

Video-based copyright infringement, involving unauthorized replication,
sharing, or broadcasting of video content, poses a significant challenge.
Despite the need for more enhanced detection methods, the challenge of video-
based copyright infringement has catalyzed the creation of innovative
solutions focused on identifying and thwarting unauthorized video usage.

Agrawal et al. present an improved derivative of the content ID system for
detecting copyright infringement in music videos on YouTube, leveraging a
combination of semantic analysis and discriminatory features (Agrawal and
Sureka, 2013). Specifically, a test dataset of 100 song queries was created,
with 1533 videos labeled as violating copyright and 151 as original, protected
videos. The videos were then classified based on discriminatory features such
as domain-specific keywords, subscriber count, and page-hit counts. This
approach alone initially achieved an accuracy of around 60% in discriminating
between original and violated videos, signifying a low confidence and
attribution rate based on features of the video. However, Agrawal et al.
improve upon this with an Individual Relevance vs. Irrelevance Video
Classifier (IRVC) and an Original vs. Copyright Infringed Video Classifier
(OCIVC) based on the proposed approach, which achieved varying rates above 75%
in accuracy and attribution.

Table 2. Examples of Crowd Votes. Types of Crowd Votes |  Example Chat Messages
---|---
Colluding Vote |  edgar dlc: change title please so nba wont copyright
Content Relevance Vote |  Joshua Colinet: As a lakers fan, I’m hoping the new look cavs flop so we can get a higher draft pick
Video Quality Vote |  KING BAMA: Looks good but can we please get some sound buddy?!!
Negativity Vote |  PHXmove: FAKE DO NOT BOTHER

The content ID system was then criticized and expanded upon by Zhang et al. in
their crowdsourcing-based copyright infringement detection (CCID) (Zhang et
al., 2018b). Their method narrows in on detecting infringement during live
video streams of basketball and soccer games. Specifically, they weigh
categorized chat messages, combine the results with metadata analysis, and
utilize a supervised binary classifier to predict instances of copyright
infringement based on the messages. Table 2 showcases four types of live chat
message types, where messages related to collusion, video quality, and
negativity carry the highest weight, and messages related to content relevance
carry the least weight. Weight in this case is expressed as

(1) $\phi_{i,k}=\text{Pr}(V_{i}=T|SV_{k})$

where $V_{i}=T$ is a binary value representing infringement or not, and
$SV_{k}$ represents the message and its weight value. By aggregating weights
and metadeta, upon testing the results with various methods, they show that
combining CCID with AdaBoost achieves the best performance, with an 86.21%
accuracy on the basketball set, and a 91.03% accuracy on the soccer set. This
not only demonstrates superior performance with the content ID system method,
but also outperforms it with its high false positive rate. Furthermore,
although Zhang et al. also exhibit a degree of subjectivity with the
categorization of messages, their method showcases a more novel performance in
contrast to Agrawal et al.’s approach, especially in an unpredictable, real-
time setting. However, advancing beyond metadata analysis towards more real-
time feature analysis against copyrighted works requires further research to
achieve more objective and accurate detection methods.

##  3\. Methods for Copyright Protection in AI

Given these approaches to detecting instances of copyright infringement across
text, images, and videos, numerous methods have been developed and employed to
protect these mediums, some of which leave room for further improvement. This
section goes into the comparisons and techniques of such methods.

###  3.1. Methods of Protecting Copyrighted Multimedia in AI

####  3.1.1. Watermarking

Methods of watermarking videos and images involve sophisticated techniques
that embed invisible or barely perceptible markers into digital content. In
comparison, the deep learning-based video watermark technique from Zhang et
al. introduce a deep learning-based watermarking technique using a custom
attention-based mechanism, encoding a D-bit data vector into a video with $T$
frames and training a model with an attention module to embed the watermark
(Zhang et al., 2019b). Mathematically, the attention module is defined as
follows:

$\begin{cases}a=\text{Conv}_{3\rightarrow 32}(V)\\\
b=\text{Conv}_{32\rightarrow D}(a)\\\
\mathcal{T}(V)=\text{Softmax}(b)\end{cases}$

where $V$ represents the source frames of the video input, $a$ represents the
result of applying a 3D convolution to V, $b$ represents the result of
applying another 3D convolution to $a$, and $\mathcal{T}(V)$ denotes the
normalized tensor values after applying Softmax to $b$. Experiments illustrate
how this methodology robustly maintains the watermark’s integrity against
alterations such as cropping and scaling, recovering a 32-bit watermark with
99% accuracy post-transformation.

In contrast, Zheng et al. propose a pioneering video copyright protection
scheme combining double watermarking techniques with blockchain technology
(Pilkington, 2016) to ensure authenticity, traceability, and security in
digital content distribution (Zheng et al., 2021). Double watermarking
involves embedding robust and fragile watermarks into the video content, where
the robust watermark ensures copyright protection, while the fragile watermark
facilitates tamper detection and integrity verification. By integrating double
watermarking with decentralization, the combined method achieved a 90%
precision rate and a 95% recall rate in detecting tampered parts of
watermarked videos against adversarial attacks.

Transitioning from video to image content, Ernawan et al. employ Tchebichef
moments to implement a more robust and improved watermarking technique on
images (Ernawan and Kabir, 2019). Tchebichef moments describe the spatial
distribution of an image’s intensity, which is defined as the following:

(2) $T_{pq}=\sum_{x=0}^{N-1}\sum_{y=0}^{N-1}t_{p}(x)\cdot t_{q}(y)\cdot
I(x,y)$

where $pq$ represent the order with respect to $x$ and $y$, $t_{p}(x)$ and
$t_{p}(y)$ are the Tchebichef polynomials of $p$ and $q$ at points $x$ and $y$
respectively, and $I(x,y)$ is the intensity of the image at point $(x,y)$
(Mukundan et al., 2001). The process begins with block division, where the
host image is partitioned into non-overlapping blocks. Tchebichef moments are
then calculated for each block, with blocks exhibiting lower visual entropies
prioritized for watermark embedding. Lastly, the image undergoes scrambling
using techniques like the Arnold transform to enhance security before the
scrambled watermark is embedded into the Tchebichef moments of selected image
blocks. Consequently, this method demonstrated resilience against noise
disruptions and JPEG2000 compression, while preserving a high peak signal-to-
noise ratio (PSNR) of approximately 40 dB.

Building on the concept of embedded watermarks, Sanivarapu et al. present a
robust digital watermarking system integrating cryptographic techniques which
safeguard images from copyright infringement (Sanivarapu et al., 2022). The
system first embeds a QR code watermark in the transform domain using Discrete
Wavelet Transform (DWT), defined by

(3) $DWT(I)=\sum_{k=-\infty}^{\infty}h(k)\cdot
I(2t-k)+\sum_{k=-\infty}^{\infty}g(k)\cdot I(2t-k)$

Where $k$ is an index that runs over a set of integer values, $h(k)$ and
$g(k)$ are the low-pass and high-pass filter coefficients, and $I(2t-k)$
represents the image pixels after filter processing (Skodras, 2003). Then it
layers on the transformed matrix with Singular Value Decomposition (SVD) as
defined by $M=U*S*V^{T}$ where $U$ and $V$ are orthonormal matrices, $S$ is
the matrix with positive values in descending order, and $T$ represents the
transpose operation upon the image. Lastly, the RSA algorithm is utilized for
watermark embedding, where the coefficients of DWT are modified using secret
keys to embed the watermark.

In comparison, the deep learning-based video watermark technique from Zhang et
al. showcases the power of attention mechanisms in preserving watermark
integrity under video transformations, which may be seen in attempts of
copyright infringement. On the other hand, Zheng et al.’s combined double
watermark and blockchain method not only ensures protection under such
transformations, but also introduces a layer of security and traceability
within the digital landscape. As for image watermarking, the method by
Sanivarapu et al. emphasizes the importance of encryption, merging traditional
watermarking with modern cryptographic techniques. However, this is at the
expense of sacrificing imperceptibility, which Ernawan et al.’s application of
Tchebichef moments offers in addition to a mathematically rigorous yet
effective solution. Future areas of improvement could focus on developing more
resilient and imperceptible watermarks that could be implemented in the
digital landscape.

####  3.1.2. Fingerprinting

Unlike watermarking which embeds a mark directly into the content,
fingerprinting analyzes the content to generate a distinctive signature that
can be detected to track unauthorized use of content. At its basis,
fingerprinting can be understood as:

(4) $F=H(C\,||\,T\,||\,K)$

where $F$ represents the fingerprint, $H$ is a cryptographic hash function,
$C$ is the content being fingerprinted, $T$ is a timestamp to ensure the
fingerprint’s uniqueness, and $K$ is a secret key of the owner.

In one example, Megias et al. propose decentralized fingerprinting as an
approach to safeguarding images from copyright infringement by embedding
unique identification sequences directly into the digital content (Megías et
al., 2020). The process generates a distinct fingerprint for each recipient,
ensuring traceability of unauthorized redistribution back to its source.
Moreover, decentralized fingerprinting prioritizes anonymity and privacy for
buyers, adhering to regulations such as the General Data Protection Regulation
(GDPR) while retaining the ability to revoke privacy in cases of copyright
infringement.

Building on the concept of decentralized fingerprinting, Kim et al. take a
nuanced approach by integrating both watermarking and fingerprinting
techniques to enhance image security (Kim et al., 2006). Their method not only
incorporates the generation and embedding of unique digital fingerprints into
images but also employs DWT for preprocessing, ensuring each image is tailored
to individual recipients similar to Megias et al.’s strategy. However, this
method extends decentralization by storing the fingerprinted images in an
archive and utilizing cookies for creating client-specific fingerprints upon
download requests. The extraction process, which leverages DWT and detailed
comparisons with a fingerprint archive, merges watermarking and fingerprinting
to enhance copyright protection’s robustness and precision, while maintaining
the traceability and anonymity found in decentralized methods. This
convergence of watermarking and fingerprinting techniques, underscored by the
strong commitment to user privacy, sets a promising foundation for future
advancements in the field of digital copyright protection.

####  3.1.3. Machine Unlearning

Figure 4. The process of machine unlearning

An increasingly adopted method for safeguarding copyrighted data in generative
AI involves machine unlearning, which focuses on erasing specific samples from
a training dataset so as to “forget” the data (Xu et al., 2023). Figure 4
visualizes the process of machine unlearning within a trained model,
extracting the target data to forget while maintaining similar capabilities
between the original and unlearned model. This process is especially crucial
in scenarios where the presence of certain data points can significantly
impact the model’s accuracy or integrity.

As an example, Zhang et al. present a suite of machine unlearning techniques
designed to shield images from copyright infringement within generative AI
systems (Zhang et al., 2023b). These techniques, including Elastic Weight
Adjustment (EWC) (Kirkpatrick et al., 2017), Decreasing Moment Matching (DMM)
(Scarciotti and Astolfi, 2017), Synaptic Consolidation (Sarfraz et al., 2022),
Catastrophic Unlearning (Zhang et al., 2023d), and Incremental Moment Matching
(IMM) (Lee et al., 2017), target the removal of specific data from AI models.
With a focus on image datasets labeled with copyright information, the
approaches vary in their mechanisms and outcomes. EWC, for instance, fine-
tunes AI models by adjusting critical weights, thus selectively erasing
copyrighted materials without affecting other learned data (Zhang et al.,
2023b). DMM, on the other hand, employs Gaussian distributions to align model
parameters’ moments, targeting the excision of copyrighted images from
training sets (Zhang et al., 2023b). Both Synaptic Consolidation and
Catastrophic Unlearning address the challenge of catastrophic forgetting,
ensuring the deletion of copyrighted content doesn’t compromise the model’s
overall performance (Sarfraz et al., 2022). Lastly, IMM enhances the system’s
resilience by aligning posterior distribution moments across tasks,
solidifying the framework’s capability to protect copyrights (Lee et al.,
2017). Together, these strategies form a comprehensive technical foundation
for safeguarding intellectual property rights in the evolving landscape of
generative AI.

####  3.1.4. Data Deduplication

Similar in vein to machine unlearning, data deduplication involves the removal
of redundant data within a model’s training dataset, leaving only unique data
remaining (Chhabra and Bala, 2018). Data deduplication algorithms can
efficiently identify duplicate files or chunks of data within a dataset (Xia
et al., 2016). By comparing hashes or other unique identifiers, the
proliferation of copyrighted content without proper authorization can be
effectively controlled (Meyer and Bolosky, 2012).

One particular way to execute secure deduplication can be through blockchain
methods. Aparna et al. explore various methods to implement deduplication in
cloud storage spaces in order to make image storage more efficient (Aparna et
al., 2020). The main method they utilize is the authorization of uploaded data
through a decentralized system commonly known as blockchain. Specifically,
each image uploaded to the network is verified and stored in blocks on the
blockchain, ensuring data integrity and ownership rights. Another approach
mentioned in the study is implementing a SHA-256 algorithm which generates a
unique hash value for each uploaded file. When a new image is uploaded, its
SHA-256 hash value is calculated. This hash value is then compared with the
hash values of previously stored images to identify duplicates. If a match is
found, indicating a duplicate image, an error is shown and the system avoids
storing the duplicate, referencing the existing image instead. By leveraging
these methods, the system can effectively identify and handle duplicate images
while ensuring copyright protection and data security.

####  3.1.5. Deterring Style Transfer

Style transfer reimagines an image in various styles by extracting its
semantic content and applying texture transfer to render it in the style of
another image (Gatys et al., 2016). This creates a result that looks like the
target image, but retains the objects and scenery of the content image. As
such, several methods have been developed to protect the integrity and
copyrights of artists’ work in the form of attacking such style transfer
methods.

In one method, Liang et al. propose a method called AdvDM using adversarial
examples to disrupt the ability of AI models to replicate original styles
(Liang et al., 2023b). These examples come in the form of small perturbations
added to images to deceive AI models, utilizing a Monte-Carlo estimation
technique to generate these adversarial examples specifically tailored for
Diffusion Models (DMs). By strategically adding perturbations to images, AdvDM
ensures that conditionally-generated images based on adversarial examples
differ significantly from the original images, effectively preventing the
imitation of art styles and subsequently protecting their copyrights.

Similarly, in another emerging approach, Shan et al. develop a perturbance
method called Glaze which allows artists to apply a minimally perceptible
layer to their artwork in order to mislead generative AI models from mimicking
the artists themselves (Shan et al., 2023). As a result, upon an adversarial
user inputting a prompt requesting to generate artwork in a protected style,
the generative model outputs a skewed result different from the adversarial
user’s request. The method was fine-tuned with artists in mind, as Shan et al.
conducted a study with over 1,156 participants, most of whom were artists.
After the study and testing the method upon the surveyed participants’ work,
93% of artists within the study reported the glazing method as successful
under a variety of robust generative models and settings, including those
which Glaze has not seen or trained on before.

While both AdvDM and Glaze effectively deter style mimicry, Glaze’s artist-
centric development and extensive validation with a broad artist community
highlight a potential direction for more personalized and artist-inclusive
copyright protection strategies. Moving forward, exploring the integration of
such perturbation methods with real-time AI monitoring systems and user-
centric focus could offer new avenues for robust, adaptable copyright
safeguarding in the evolving digital art sphere.

###  3.2. Methods of Protecting Copyrighted Text in AI

This section delves into the methods employed to safeguard copyrighted text
within the realm of generative AI. From advancements in natural language
processing to innovative cryptographic solutions, the following strategies
offer insights into how AI can be leveraged to both create and protect
copyrighted text.

####  3.2.1. Digital Signatures and Hashing

Digital signatures and content hashing create a robust framework for copyright
protection in textual content, leveraging both cryptographic and hash function
methodologies to ensure content integrity and authorship authentication. Chain
and Kuo’s approach utilizes chaotic maps for digital signatures, which exploit
the unpredictable nature of chaotic systems to secure text against copyright
infringement (Chain and Kuo, 2013). These systems are ideal for cryptography
due to their sensitive dependence on initial conditions, generating unique
signatures from plaintext via chaotic map transformations and cryptographic
operations. Concurrently, the Elliptic Curve Digital Signature Algorithm
(ECDSA) offers an advanced cryptographic framework known for its security and
efficiency (Chandrashekhara et al., 2021). ECDSA employs elliptic curve
cryptography combined with digital signatures, where a private key generates a
public key for signature verification. The content is hashed using algorithms
like SHA-256, creating a 256-bit hash value that acts as a digital fingerprint
(Appel, 2015). This unique identifier facilitates content authentication,
plagiarism detection, and tamper evidence, crucial for protecting copyrighted
text. Together these approaches highlight a sophisticated mechanism for
textual copyright protection. It not only authenticates the content’s origin
and integrity, but also provides a verifiable claim of ownership for content
creators, offering a dual layer of protection and addressing the urgent need
for reliable copyright enforcement in the digital era.

####  3.2.2. Blockchain Technology

Blockchain technology offers a novel approach to copyright protection for
textual content, utilizing its decentralized and immutable ledger to ensure
secure and permanent records of text creation and ownership. This system is
particularly suited to safeguarding written works from copyright infringement
by recording transactions in blocks that are cryptographically linked,
ensuring that once a piece of text is entered into the blockchain, it remains
unaltered and traceable (Qureshi and Megías Jiménez, 2021). The addition of
digital signatures or hash values acts as a digital fingerprint for texts,
providing a transparent and tamper-proof method for timestamping content at
the moment of creation, thereby offering irrefutable evidence of authorship
and integrity (Jiang et al., 2020).

By eliminating central points of control, blockchain technology enhances
security against unauthorized access and alterations, making it a resilient
system for copyright management in the digital domain. It enables authors,
copyright holders, and the public to verify the authenticity and originality
of texts without compromising privacy. Given its capacity to maintain clear,
accessible records of copyright claims and ownership, blockchain presents a
promising future for copyright protection of textual content, encouraging
innovation and fair use in the increasingly digital landscape of literary
creation.

###  3.3. Limitations of Methods Protecting Copyright Misuse

The array of methods aimed at protecting copyrighted content within AI systems
presents a spectrum of advantages and inherent limitations. Watermarking
techniques, for instance, stand out for their ability to establish content
ownership. However, they grapple with vulnerabilities such as the ease of
altering watermarks, potentially compromising the security they aim to
provide. On the other hand, decentralized fingerprinting methods underscore
privacy and reduce centralized control risks, yet they face obstacles in
efficiently managing and identifying copyright infringements due to the
distributed nature of their storage and the potential for false negatives in
detection.

Transitioning from watermarking and fingerprinting to more dynamic solutions,
machine unlearning offers a promising avenue by enabling the removal of
copyrighted material from AI systems. Nonetheless, this process is not without
its trade-offs, as it may inadvertently impair the AI model’s performance by
eliminating critical features and patterns learned from the data, leading to a
reduction in overall effectiveness. Similarly, data deduplication aims to
prevent redundancy and unauthorized content replication, but it struggles with
adaptability and maintaining accuracy, often risking false positives that
could unjustly flag legitimate content. Blockchain technology, while heralding
a new era of secure and transparent content management, encounters its own set
of challenges related to scalability, transaction speed, and navigating the
complex landscape of data privacy laws, which can deter its application to
copyright protection.

These varied methodologies, each with their specific set of challenges—ranging
from susceptibility to manipulation, privacy concerns, performance
degradation, to legal and scalability issues—underscore the necessity for
continued research and innovation. Developing solutions that not only address
the limitations of current methods but also anticipate the evolving
capabilities of generative AI models is crucial.

##  4\. Resources for Evaluating Copyright Violation

Assessing the degree of copyright infringement and adherence to fair usage
laws remains a nuanced challenge, particularly given the subjective nature of
human interpretation. This section explores resources, datasets, and metrics
essential for the development of tools aimed at evaluating copyright
infringement levels.

###  4.1. Datasets

AI-generated: AI-generated datasets offer a comprehensive platform for
evaluating the intricacies of AI-crafted content and its alignment with
copyright regulations. The AI-ArtBench dataset, for instance, incorporates
over 180,000 images, merging 60,000 human-made artworks with 120,000 AI-
generated pieces to present a broad spectrum of artistic styles for model
development (Silva and Bird, 2023). The CIFAKE dataset further explores the
boundary between synthetic and real with its blend of generated images and
genuine photographs, enriching our understanding of AI’s capacity for
realistic object depiction (Bird, 2023; Bird and Lotfi, 2024). Additionally,
the TWIGMA dataset compiles an extensive collection of Twitter images,
exceeding 800,000 entries, facilitating discussions on copyright issues within
social media (Papineni et al., 2002). Textually, datasets from GPT-2 and
Arxiv-NLP (Liyanage et al., 2022) showcase the evolving prowess of AI in
emulating human writing, a trend further exemplified by the GPT-Wiki-Intro
dataset’s side-by-side comparison of Wikipedia introductions and their AI
counterparts (Bhat, Aaditya, 2023).

Human-made: In parallel, human-made datasets serve as essential benchmarks for
contrasting against AI-generated material. The COCO dataset, with its vast
array of human-captured imagery, sets a foundational standard for such
comparisons (Lin et al., [n. d.], 2015). This is complemented by Flickr30k
(Young et al., 2014) and the OpenImages dataset (Kuznetsova et al., 2018),
which collectively offer over 9 million annotated images, providing a deep
dive into the diversity of human creativity. Noteworthy contributions from art
repositories, including over 400,000 works from the Metropolitan Museum of Art
(of Art, [n. d.]) and WikiArt (WikiArt, 2022), further the available resources
for comparative analysis. The textual domain is equally robust, with datasets
like OpenSubtitles (Lison and Tiedemann, 2021) and BookCorpus (Bandy and
Vincent, 2021a) delivering billions of sentences and insights into a variety
of linguistic expressions. WikiText’s extensive collection (Merity et al.,
2016b) from Wikipedia articles, and the accessibility of JSTOR’s library
(JSTOR, [n. d.]) for deep copyright evaluations, underscore the depth of
human-generated academic content available for study.

###  4.2. Metrics for Copyright Violations

Metrics for assessing copyright violations quantify the similarity between AI-
generated and copyrighted content across various domains. Cosine similarity
measures the orientation of feature vectors, where a higher value signifies
greater similarity (Lahitani et al., 2016). This metric can potentially be
combined with the feature similarity index (Zhang et al., 2011) to provide a
quantifiable analysis of copyright infringement in images. Additionally, the
Jaccard similarity evaluates the overlap between two sets, useful for
analyzing unique word overlaps in texts or segmented regions in images,
providing insight into the similarity of content composition (Niwattanakul et
al., 2013; Ogwok and Ehlers, 2022). Lastly, the BLEU score, while originally
designed for machine translation, measures the precision of n-gram overlaps
between generated and reference texts, indicating the extent of language
structure and usage similarity (Papineni et al., 2002).

###  4.3. Web Tools Determining Plagiarized Content

Over the past couple years, tools have been made to assist users in detecting
generated and plagiarized content and track where their content is reposted
without permission. However, such tools are subject to false positives and
negatives due to the increasing similarity between human-generated and AI-
generated content, and are subsequently imperfect for reliability in
detection. One way to mitigate this for images at the moment is through the
use of search engines with reverse lookup options such as Yandex (Yandex, [n.
d.]), TinEye (TinEye, [n. d.]), and Google Images (Google, [n. d.]). By using
reverse image search, users can find potentially find instances of where their
images and art are reposted and attribute copyright violation. In terms of
text, websites such as Scribbr (Scribbr, [n. d.]) and Grammarly (Grammarly,
[n. d.]) can detect and attribute where a body of text has been written
elsewhere and attribute plagiarism.

##  5\. Discussion of Regulations for Generative AI in Society

U.S. Regulations and Cases: Under the current U.S. legal framework, as
enshrined by the Constitution, copyright protection is explicitly reserved for
works authored by humans, highlighting a significant hurdle in securing
copyright for AI-generated creations (Kariyawasam, 2021; Zirpoli, 2023). This
legal stance reflects a broader principle within intellectual property law,
aimed at encouraging human innovation and creativity, suggesting that AI falls
outside the scope of protection.

Furthermore, the U.S. Copyright Office’s definition of copyright infringement
emphasizes the unauthorized use of copyrighted material, yet the applicability
of these definitions for AI-generated works remains mired in ambiguity,
especially when considering the nuances of fair use and derivative works
(Office, 2008; Team, 2022). This ambiguity, however, is clarified in Zirpoli’s
analysis from the Congressional Research Service, which emphasizes that only
human-created works qualify for copyright from the Copyright Office, leaving
AI-generated content in a precarious position unless it incorporates
significant human input or discloses its AI-driven origins (Zirpoli, 2023).
These legal paradigms underscore the evolving challenges and complexities in
delineating the boundaries of copyright protection in the age of AI.

International Copyright Laws and Frameworks: In an international context,
Kariyawasam examines the nuances of copyright legislation in Australia and its
comparison with other jurisdictions (Kariyawasam, 2021). In Australia, Section
32 of the Copyright Act 1968 stipulates that a work should be created by a
qualified human to be considered original (Kariyawasam, 2021). This
requirement aligns with the principles of the Berne Convention, where
authorship is reserved for humans as a testament to individuality and
uniqueness. However, the United Kingdom appears to adopt a different approach,
as evidenced by the sale of an AI-generated painting called Edmond Belamy in
2018, where authorship was attributed to the developer of the algorithm rather
than the AI itself (Kariyawasam, 2021). Similarly, New Zealand’s Copyright Act
of 1994 recognizes the person who makes the necessary arrangements for
computer-generated works as the author, thereby granting copyright even when
the work is generated through providing instructions or parameters to an AI
system (Kariyawasam, 2021).

As in Europe, Franceschelli and Musolesi address pertinent questions
surrounding the use of copyrighted data in AI training (Franceschelli and
Musolesi, 2022a). They acknowledge that deep learning techniques often rely on
substantial amounts of copyrighted data. Furthermore, they speculate that
generative techniques may qualify as expressive use under US copyright law, as
they involve the re-expression of original authors’ ideas through learning and
influence in the training process. Notably, they highlight an exception in EU
law that permits research organizations and cultural heritage institutions to
reproduce protected works for text and data mining, provided the author has
not explicitly reserved copyright (Franceschelli and Musolesi, 2022a).

Potential Regulations: In the current landscape of 2024, the lack of clear
regulations for AI is increasingly evident, fostering environments ripe for
misinformation and the creation of harmful content. Weaver highlights the
critical need for a structured approach to AI governance, suggesting that
without legislative oversight, the unchecked expansion of AI could lead to
negative societal impacts and the concentration of power within the private
sector (Weaver, 2018). Drawing from historical precedents in industries like
fantasy football and tobacco, Weaver warns of the potential for resistance
against future regulations from well-established interests.

In response to these challenges, the National AI Advisory Committee proposes a
balanced regulatory scheme, introducing concepts such as licensing and
taxation to manage the use and development of AI (Ballantyne et al., 2023).
Their proposal aims to strike a balance, fostering innovation while mitigating
AI’s risks. While they caution against the stifling effects of stringent
regulations on innovation, their framework provides a starting point for
policymakers aiming to harness AI’s benefits while mitigating its risks.

Building upon the Committee’s recommendations, Erdelyi and Goldsmith advocate
for a global regulatory framework to address the proliferation of AI
technologies worldwide (Erdélyi and Goldsmith, 2018). This approach seeks to
provide the necessary legal backbone for AI regulation while maintaining the
adaptability required for international application. Acknowledging the rapid
pace of AI advancements, their proposal also addresses challenges of
enforcement and the risk posed to nations with less technological
infrastructure. By advocating for a unified, flexible framework, they aim to
navigate the complexities of global AI governance, ensuring effective
regulation across borders. This progression from national recommendations to a
global framework underscores the critical need for cohesive approaches to AI
regulation, highlighting the international community’s role in shaping the
future of AI governance.

##  6\. Challenges and Future Directions

The evolution of generative AI introduces multifaceted challenges and beckons
a variety of future research directions to ensure copyright compliance and
foster innovation. Beyond developing tools for distinguishing AI-generated
content from human-created work, there is a burgeoning need for machine
learning models that can effectively navigate the nuances of creative
authorship. Enhancing metadata and leveraging digital fingerprints to trace AI
origins represents a vital step toward accountability and transparency in AI-
generated content. Moreover, the velocity of AI advancements outpacing
existing copyright laws underscores a critical gap in our legal frameworks,
necessitating agile legal scholarship and adaptive policymaking. Examining the
ethical aspects of AI creation highlights the risk of AI systems reflecting or
intensifying biases from their training data, underscoring the need for
creating fair and unbiased AI models.

Additionally, the exploration of copyright issues in collaborative works
involving AI transitions naturally into broader discourse on the need for
international harmony in copyright laws. As we delve into the complexities of
attributing authorship in creations that blend human ingenuity with AI’s
capabilities, we uncover the potential for pioneering copyright
classifications. These new guidelines aim to navigate the unique contributions
of both humans and AI in the creative process. This nuanced understanding of
collaborative works serves as a foundation for extending the dialogue beyond
individual jurisdictions to the international arena.

Building off international jurisdiction, the digital realm’s inherent lack of
borders accentuates the necessity for a cohesive approach to copyright norms.
It is in this context that the question of authorship and contribution in AI-
assisted creations becomes part of a larger narrative about global
cooperation. The pursuit of unified global standards or treaties that address
the specifics of generative AI copyright issues embodies this shift. Such
international efforts would not only harmonize copyright laws but also ensure
that creators everywhere enjoy consistent protection and recognition for their
work, bridging gaps between disparate legal systems and fostering a global
community of innovation and respect for intellectual property. Moreover, the
conversation on copyright in generative AI extends into the realm of public
perception and acceptance, highlighting the need for public education and
engagement strategies to demystify AI’s role in content creation. Educating
content creators, consumers, and policymakers about the capabilities and
limitations of generative AI can foster an environment of informed decision-
making and constructive dialogue.

Lastly, the advent of decentralized technologies like blockchain presents
novel avenues for copyright management and protection in AI-generated content.
Investigating blockchain’s potential to securely and transparently manage
copyright claims and verify content authenticity could revolutionize copyright
enforcement mechanisms. Addressing these challenges requires a
multidisciplinary approach, uniting technologists, legal experts, ethicists,
and policymakers. Through collaboration, they have the power to shape a future
in which copyright laws both protect human ingenuity and embrace the
groundbreaking capabilities of generative AI, forging a balance that nurtures
innovation while ensuring fair access to digital content.

##  7\. Conclusion

Generative AI presents a complex challenge in navigating copyright issues, as
AI-generated content can often infringe upon existing copyrights. This survey
paper comprehensively explores approaches and regulations to consider in
navigating copyright issues in AI. Specifically, it details methods for
detecting AI-generated copyright violations. It then delves into existing
approaches, limitations, and potential biases in detection methods, as well as
various practical approaches to protecting copyrighted content in AI,
including but not limited to watermarking, digital signatures, and machine
unlearning. Additionally, resources such as AI-generated datasets and web
tools are outlined for users to assess copyright infringement in online
multimedia. Lastly, the paper examines regulations surrounding copyright and
AI-generated works, offering valuable insights for future research and policy
development in this evolving field. As we stand at the intersection of
creativity and technology, the significance of these methods cannot be
overstated, setting a critical foundation for future advancements ensuring a
balanced coexistence of generative AI and data integrity.

## References

  * (1)
  * Abbott and Rothman (2022) Ryan Abbott and Eliza Rothman. 2022.  Disrupting Creativity: Copyright Law in the Age of Generative Artificial Intelligence.  _SSRN_ (2022).
  * Agrawal and Sureka (2013) Swati Agrawal and Ashish Sureka. 2013.  Copyright Infringement Detection of Music Videos on YouTube by Mining Video and Uploader Meta-data. In _Big Data Analytics_ , Vasudha Bhatnagar and Srinath Srinivasa (Eds.). Springer International Publishing, Cham, 48–67.
  * Anzelmi et al. (2011) Daniele Anzelmi, Domenico Carlone, Fabio Rizzello, Robert Thomsen, and Dil Muhammad. 2011.  Plagiarism Detection Based on SCAM Algorithm. In _International MultiConference of Engineers and Computer Scientists (IMECS) 2011_ , Vol. 1. 272–277.  https://www.iaeng.org/publication/IMECS2011/IMECS2011_pp272-277.pdf
  * Aparna et al. (2020) R. Aparna, Roopa G. Kulkarni, and Shilpa Chaudhari. 2020\.  Secure Deduplication for Images using Blockchain. In _IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)_.
  * Appel (2015) Andrew W. Appel. 2015\.  Verification of a Cryptographic Primitive: SHA-256.  _ACM Transactions on Programming Languages and Systems_ 37, 2, Article 7 (apr 2015), 31 pages.  https://doi.org/10.1145/2701415
  * Bai et al. (2012) Liantao Bai, Yuegong Zhang, and Guoqiang Yang. 2012\.  SM2 cryptographic algorithm based on discrete logarithm problem and prospect. In _2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet)_. 1294–1297.  https://doi.org/10.1109/CECNet.2012.6201878
  * Ballantyne et al. (2023) Amanda Ballantyne, Jack Clark, Victoria Espinel, Janet Haven, Daniel E. Ho, Ashley Llorens, Frank Pasquale, Christina Montgomery, and Navrina Singh. 2023.  Rationales, Mechanisms, and Challenges to Regulating AI: A Concise Guide and Explanation.  National AI Advisory Committee (NAIAC) Working Group on Regulation and Executive Action.
  * Bandy and Vincent (2021a) John Bandy and Nicholas Vincent. 2021a.  https://huggingface.co/datasets/bookcorpus
  * Bandy and Vincent (2021b) John Bandy and Nicholas Vincent. 2021b.  Addressing ”Documentation Debt” in Machine Learning: A Retrospective Datasheet for BookCorpus. In _Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks_ , J. Vanschoren and S. Yeung (Eds.), Vol. 1. Curran.  https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/54229abfcfa5649e7003b83dd4755294-Paper-round1.pdf
  * Baraheem and Nguyen (2023) Samah S. Baraheem and Tam V. Nguyen. 2023.  AI vs. AI: Can AI Detect AI-Generated Images?  _J. Imaging_ 9, 10 (2023).  https://doi.org/10.3390/jimaging9100199
  * Bhat, Aaditya (2023) Bhat, Aaditya. 2023\.  GPT-wiki-intro (Revision 0e458f5).  https://doi.org/10.57967/hf/0326
  * Bird (2023) Jordan J. Bird. 2023\.  CIFAKE: Real and AI-Generated Synthetic Images.  https://www.kaggle.com/datasets/birdy654/cifake-real-and-ai-generated-synthetic-images
  * Bird and Lotfi (2024) Jordan J. Bird and Ahmad Lotfi. 2024.  CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images.  _IEEE Access_ 12 (2024), 15642–15650.  https://doi.org/10.1109/ACCESS.2024.3356122
  * Brittain (2024) Blake Brittain. 2024\.  OpenAI gets partial win in authors’ US copyright lawsuit — Reuters.  https://www.reuters.com/legal/litigation/openai-gets-partial-win-authors-us-copyright-lawsuit-2024-02-13/
  * Cai et al. (2023) Yu Cai, Drew Youngstrom, and Wenbin Zhang. 2023.  Exploring Approaches for Teaching Cybersecurity and AI for K-12. In _2023 IEEE International Conference on Data Mining Workshops (ICDMW)_. IEEE, 1559–1564.
  * Chadwick (2024) Lauren Chadwick. 2024\.  AI Models Need Copyrighted Content for training, OpenAI Argues.  https://www.euronews.com/next/2024/01/09/openai-says-its-impossible-to-train-ai-without-copyrighted-materials
  * Chain and Kuo (2013) Kai Chain and Wen-Chung Kuo. 2013.  A new digital signature scheme based on chaotic maps.  _Nonlinear Dynamics_ 74 (12 2013).  https://doi.org/10.1007/s11071-013-1018-1
  * Chandrashekhara et al. (2021) J. Chandrashekhara, Anu V B, Prabhavathi H, and Ramya B R. 2021\.  A COMPREHENSIVE STUDY ON DIGITAL SIGNATURE.  _International Journal of Innovative Research in Computer Science & Technology_ 9, 3 (May 2021).  https://doi.org/10.21276/ijircst.2021.9.3.7
  * Chen et al. (2021) Jialuo Chen, Jingyi Wang, Tinglan Peng, Youcheng Sun, Peng Cheng, Shouling Ji, Xingjun Ma, Bo Li, and Dawn Song. 2021.  Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models.  _IEEE_ (2021).
  * Chen and Zou (2023) Yiqun Chen and James Zou. 2023.  TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter.  arXiv:2306.08310 [stat.AP]
  * Chhabra and Bala (2018) Nipun Chhabra and Manju Bala. 2018.  A Comparative Study of Data Deduplication Strategies.  , 68-72 pages.  https://doi.org/10.1109/ICSCCC.2018.8703363
  * Chinta et al. (2023) Sribala Vidyadhari Chinta, Karen Fernandes, Ningxi Cheng, Jordan Fernandez, Shamim Yazdani, Zhipeng Yin, Zichong Wang, Xuyu Wang, Weifeng Xu, Jun Liu, et al. 2023\.  Optimization and Improvement of Fake News Detection using Voting Technique for Societal Benefit. In _2023 IEEE International Conference on Data Mining Workshops (ICDMW)_. IEEE, 1565–1574.
  * Chu et al. (2023) Timothy Chu, Zhao Song, and Chiwun Yang. 2023.  How to Protect Copyright Data in Optimization of Large Language Models?  _arXiv_ (2023).
  * Chu et al. (2024) Zhibo Chu, Shiwen Ni, Zichong Wang, Xi Feng, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang, and Wenbin Zhang. 2024.  History, Development, and Principles of Large Language Models-An Introductory Survey.  _arXiv preprint arXiv:2402.06853_ (2024).
  * Chu et al. ([n. d.]) Zhibo Chu, Zichong Wang, and Wenbin Zhang. [n. d.].  Fairness in Large Language Models: A Taxonomic Survey.  ([n. d.]).
  * Cui et al. (2023b) Wanyun Cui, Linqiu Zhang, Qianle Wang, and Shuyang Cai. 2023b.  Who Said That? Benchmarking Social Media AI Detection.  _arXiv.org_ (Oct 2023).  https://doi.org/10.48550/arXiv.2310.08240
  * Cui et al. (2023a) Yingqian Cui, Jie Ren, Han Xu, Pengfei He, Hui Liu, Lichao Sun, Yue Xing, and Jiliang Tang. 2023a.  DiffusionShield: A Watermark for Copyright Protection Against Generative Diffusion Models.  _NeurIPS 2023 Workshop on Diffusion Models_ (2023).
  * Dogoulis et al. (2023a) Pantelis Dogoulis, Giorgos Kordopatis-Zilos, Ioannis Kompatsiaris, and Symeon Papadopoulos. 2023a.  Improving Synthetically Generated Image Detection in Cross-Concept Settings. In _Proceedings of the 2nd ACM International Workshop on Multimedia AI against Disinformation_. 28–35.  https://doi.org/10.1145/3592572.3592846
  * Dogoulis et al. (2023b) Pantelis Dogoulis, Giorgos Kordopatis-Zilos, Ioannis Kompatsiaris, and Symeon Papadopoulos. 2023b.  Improving Synthetically Generated Image Detection in Cross-Concept Settings.  _ACM_ (2023).
  * dos Santos and Rocha (2020) Cláudio Lisboa dos Santos and Angela Machado Rocha. 2020.  Intellectual Property on Works of Art Made by Artificial Intelligence.  _IJAERS_ 7, 12 (2020), 49–59.  https://doi.org/10.22161/ijaers.712.9
  * Epstein et al. (2023) David C. Epstein, Ishan Jain, Oliver Wang, and Richard Zhang. 2023\.  Online Detection of AI-Generated Images. In _2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)_. 382–392.  https://doi.org/10.1109/ICCVW60793.2023.00045
  * Erdélyi and Goldsmith (2018) Olivia J Erdélyi and Judy Goldsmith. 2018.  Regulating artificial intelligence: Proposal for a global solution.  _National Science Foundation_ (2018).
  * Ernawan and Kabir (2019) Ferda Ernawan and Muhammad Nomani Kabir. 2019.  An Improved Watermarking Technique for Copyright Protection Based on Tchebichef Moments.  _IEEE Access_ 7 (2019), 151985–152003.  https://doi.org/10.1109/access.2019.2948086
  * Fei et al. (2022) Jianwei Fei, Zhihua Xia, Benedetta Tondi, and Mauro Barni. 2022\.  Supervised GAN Watermarking for Intellectual Property Protection. In _IEEE International Workshop on Information Forensics and Security (WIFS)_. IEEE.
  * Franceschelli and Musolesi (2022a) Giorgio Franceschelli and Mirco Musolesi. 2022a.  Copyright in Generative Deep Learning.  _Data & Policy_ (2022).
  * Franceschelli and Musolesi (2022b) Giorgio Franceschelli and Mirco Musolesi. 2022b.  Copyright in Generative Deep Learning.  _Data & Policy_ 4 (2022), e17.  https://doi.org/10.1017/dap.2022.10
  * Gatys et al. (2016) Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. 2016.  Image Style Transfer Using Convolutional Neural Networks. In _2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_. 2414–2423.  https://doi.org/10.1109/CVPR.2016.265
  * Gaura and Sojka (2015) Jan Gaura and Eduard Sojka. 2015.  Normalised Diffusion Cosine Similarity and Its Use for Image Segmentation.  _SciTePress - Science and Technology Publications_ (Jan 2015).  https://doi.org/10.5220/0005220601210129
  * Gokaslan (2020) Aaron Gokaslan. 2020\.  OpenWebText.  https://skylion007.github.io/OpenWebTextCorpus/
  * Google ([n. d.]) Google. [n. d.].  https://google.com/images
  * Grammarly ([n. d.]) Grammarly. [n. d.].  https://grammarly.com
  * Gray and Suzor (2020) Joanne E Gray and Nicolas P Suzor. 2020.  Playing with machines: Using machine learning to understand automated copyright enforcement at scale.  _Big Data & Society_ 7, 1 (Jan 2020), 205395172091996\.  https://doi.org/10.1177/2053951720919963
  * Greenspan (2004) Gideon Greenspan. 2004\.  Copyscape Plagiarism Checker - Duplicate Content Detection Software.  https://www.copyscape.com/
  * Guo et al. (2023) Junfeng Guo, Yiming Li, Lixu Wang, Shu-Tao Xia, Heng Huang, Cong Liu, and Bo Li. 2023.  Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand. In _NeurIPS 2023_.  https://doi.org/10.48550/arxiv.2310.14942
  * Guyet et al. (2022) Thomas Guyet, Wenbin Zhang, and Albert Bifet. 2022\.  Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences. In _22nd International Conference on Computational Science_. Springer, 460–472.
  * Hayes (2023) Carol M. Hayes. 2023\.  Generative Artificial Intelligence and Copyright: Both Sides of the Black Box.  Social Science Research Network.
  * He et al. (2015) Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015\.  Deep Residual Learning for Image Recognition.  arXiv:1512.03385 [cs.CV]
  * He et al. (2022) Xuanli He, Qiongkai Xu, Yi Zeng, Lingjuan Lyu, Fangzhao Wu, Jiwei Li, and Ruoxi Jia. 2022.  CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks.  _NeurIPS 2022_ (2022).
  * (50) The International Digital Economy Academy (IDEA). 2023.  IDEA-Research/HumanArt.  https://github.com/IDEA-Research/HumanArt
  * Ji et al. (2022) Zhenlan Ji, Pingchuan Ma, and Shuai Wang. 2022.  Unlearnable Examples: Protecting Open-Source Software from Unauthorized Neural Code Learning.  _The 34th International Conference on Software Engineering and Knowledge Engineering_ (2022).
  * Jiang et al. (2023) Harry H. Jiang, Lauren Brown, Jessica Cheng, Mehtab Khan, Abhishek Gupta, Deja Workman, Alex Hanna, Johnathan Flowers, and Timnit Gebru. 2023.  AI Art and its Impact on Artists.  _Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society_ (Aug 2023).  https://doi.org/10.1145/3600211.3604681
  * Jiang et al. (2020) Tao Jiang, Aina Sui, Weiguo Lin, and Pengbin Han. 2020\.  Research on the Application of Blockchain in Copyright Protection. In _2020 International Conference on Culture-oriented Science & Technology (ICCST)_. 616–621.  https://doi.org/10.1109/ICCST50977.2020.00127
  * Joslin and Hao (2020) Matthew Joslin and Shuang Hao. 2020.  Attributing and Detecting Fake Images Generated by Known GANs. In _2020 IEEE Security and Privacy Workshops (SPW)_. 8–14.  https://doi.org/10.1109/SPW50608.2020.00019
  * JSTOR ([n. d.]) JSTOR. [n. d.].  Text-mining support.  https://about.jstor.org/whats-in-jstor/text-mining-support/
  * Ju et al. (2023) Xuan Ju, Ailing Zeng, Jianan Wang, Qiang Xu, and Lei Zhang. 2023.  Human-Art: a Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes. In _2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)_.  https://doi.org/10.1109/cvpr52729.2023.00067
  * Kariyawasam (2021) Kanchana Kariyawasam. 2021\.  Artificial intelligence and challenges for copyright law.  _International Journal of Law and Information Technology_ (2021).
  * Kim et al. (2023) Changhoon Kim, Kyle Min, Maitreya Patel, Sheng Cheng, and Yezhou Yang. 2023.  WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models. In _NeurIPS Workshop on Diffusion Models_.
  * Kim et al. (2021) Doyoung Kim, Suwoong Heo, Jiwoo Kang, Hogab Kang, and Sanghoon Lee. 2021.  A Photo Identification Framework to Prevent Copyright Infringement with Manipulations.  _Applied Sciences_ 11, 19 (Oct 2021), 91–94.  https://doi.org/10.3390/app11199194
  * Kim et al. (2006) Sangkuk Kim, Heejun Yoon, Hwamook Yoon, and Wongoo Lee. 2006\.  A robust digital fingerprinting mechanism for digital copyright protection. In _Proceedings of the 2006 International Conference on Computational Science and Its Applications - Volume Part II_ (Glasgow, UK) _(ICCSA’06)_. Springer-Verlag, Berlin, Heidelberg, 11–17.  https://doi.org/10.1007/11751588_2
  * Kirkpatrick et al. (2017) James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell. 2017.  Overcoming catastrophic forgetting in neural networks.  _Proceedings of the National Academy of Sciences_ 114, 13 (March 2017), 3521–3526.  https://doi.org/10.1073/pnas.1611835114
  * Koch et al. (2015) Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov. 2015\.  Siamese Neural Networks for One-shot Image Recognition.  _ICML Deep Learning Workshop_ 2 (2015), 1–8.  https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf
  * Kordov (2021) Krasimir Kordov. 2021\.  TEXT ENCRYPTION ALGORITHM FOR SECURE COMMUNICATION.  _International Journal of Apllied Mathematics_ 34 (08 2021).  https://doi.org/10.12732/ijam.v34i4.9
  * Krizhevsky et al. (2012) Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012\.  ImageNet Classification with Deep Convolutional Neural Networks.  _Commun. ACM_ 60, 6 (May 2012), 84–90.  https://doi.org/10.1145/3065386
  * Kuznetsova et al. (2018) Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Tom Duerig, and Vittorio Ferrari. 2018.  The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale.  _CoRR_ abs/1811.00982 (2018).  http://arxiv.org/abs/1811.00982
  * Lahitani et al. (2016) Alfirna Rizqi Lahitani, Adhistya Erna Permanasari, and Noor Akhmad Setiawan. 2016.  Cosine similarity to determine similarity measure: Study case in online essay assessment. In _2016 4th International Conference on Cyber and IT Service Management_. 1–6.  https://doi.org/10.1109/CITSM.2016.7577578
  * Lee et al. (2023) Jooyoung Lee, Thai Le, Jinghui Chen, and Dongwon Lee. 2023\.  Do Language Models Plagiarize?. In _WWW ’23: Proceedings of the ACM Web Conference_. 3637–3647.  https://doi.org/10.1145/3543507.3583199
  * Lee et al. (2017) Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, and Byoung-Tak Zhang. 2017.  Overcoming Catastrophic Forgetting by Incremental Moment Matching. In _Advances in Neural Information Processing Systems 30 (NIPS 2017)_. 1–16.
  * Li et al. (2024a) Haodong Li, Gelei Deng, Yi Liu, Kailong Wang, Yuekang Li, Tianwei Zhang, Yang Liu, Guoai Xu, Guosheng Xu, and Haoyu Wang. 2024a.  Digger: Detecting Copyright Content Mis-usage in Large Language Model Training.  https://doi.org/10.48550/arXiv.2401.00676
  * Li et al. (2023) Xiang Li, Qianli Shen, and Kenji Kawaguchi. 2023.  Probabilistic Copyright Protection Can Fail for Text-to-Image Generative Models.  _arXiv_ (2023).
  * Li et al. (2024b) Yaxin Li, Ren Jie, Xu Han, and Liu Hui. 2024b.  Neural Style Protection: Counteracting Unauthorized Neural Style Transfer.  _2024 IEEE/CVF Winter Conference on Applications of Computer Vision_ (2024).
  * Li et al. (2022) Zhiwen Li, Dan Zhang, XiaoGuang Yuan, and Jun Zheng. 2022\.  Deep Hash Model for Similarity Text Retrieval. In _2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD)_. 638–643.  https://doi.org/10.1109/ICAIBD55127.2022.9820495
  * Liang et al. (2023a) Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, and Haibing Guan. 2023a.  Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples.  (2023).
  * Liang et al. (2023b) Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, and Haibing Guan. 2023b.  Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples. In _Proceedings of the 40th International Conference on Machine Learning_ _(Proceedings of Machine Learning Research, Vol. 202)_ , Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (Eds.). PMLR, 20763–20786.  https://proceedings.mlr.press/v202/liang23g.html
  * Library of Congress, Copyright Office (2023) Library of Congress, Copyright Office. 2023\.  Artificial Intelligence and Copyright.  [Docket No. 2023–6].
  * Lin et al. (2015) Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. 2015.  Microsoft COCO: Common Objects in Context.  (2015).  arXiv:1405.0312 [cs.CV]
  * Lin et al. ([n. d.]) Tsung-Yi Lin, Genevieve Patterson, Matteo R. Ronchi, Yin Cui, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, Larry Zitnick, and Piotr Dollár. [n. d.].  Common Objects in Context (COCO).  https://cocodataset.org/.
  * Lison and Tiedemann (2016) Pierre Lison and Jörg Tiedemann. 2016.  OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In _Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)_ , Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, and Stelios Piperidis (Eds.). European Language Resources Association (ELRA), 923–929.  https://aclanthology.org/L16-1147
  * Lison and Tiedemann (2021) Pierre Lison and Jörg Tiedemann. 2021.  https://huggingface.co/datasets/open_subtitles
  * Liu et al. (2023) Zhen Liu, Ruoyu Wang, Nathalie Japkowicz, Heitor Murilo Gomes, Bitao Peng, and Wenbin Zhang. 2023\.  SeGDroid: An Android malware detection method based on sensitive function call graph learning.  _Expert Systems with Applications_ (2023), 121125.
  * Liu et al. (2021) Zhen Liu, Ruoyu Wang, Nathalie Japkowicz, Deyu Tang, Wenbin Zhang, and Jie Zhao. 2021\.  Research on unsupervised feature learning for Android malware detection based on Restricted Boltzmann Machines.  _Future Generation Computer Systems_ 120 (2021), 91–108.
  * Liyanage et al. (2022) Vijini Liyanage, Davide Buscaldi, and Adeline Nazarenko. 2022\.  A Benchmark Corpus for the Detection of Automatically Generated Text in Academic Publications. In _13th Conference on Language Resources and Evaluation (LREC 2022)_. 4692–4700.
  * Marcus and Southen (2024) Gary Marcus and Reid Southen. 2024.  Generative AI Has a Visual Plagiarism Problem - IEEE Spectrum.  https://spectrum.ieee.org/midjourney-copyright
  * Megías et al. (2020) David Megías, Minoru Kuribayashi, and Amna Qureshi. 2020\.  Survey on Decentralized Fingerprinting Solutions: Copyright Protection through Piracy Tracing.  _Computers_ 9, 2 (Apr 2020), 1–19.  https://doi.org/10.3390/computers9020026
  * Merity et al. (2016a) Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016a.  Pointer Sentinel Mixture Models.  arXiv:1609.07843 [cs.CL]
  * Merity et al. (2016b) Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016b.  WikiText-103 Dataset.  https://huggingface.co/datasets/wikitext
  * Meyer and Bolosky (2012) Dutch T. Meyer and William J. Bolosky. 2012.  A study of practical deduplication.  _ACM Transactions on Storage_ 7, 4 (Jan 2012), 1–20.  https://doi.org/10.1145/2078861.2078864
  * Midjourney (2023) Midjourney. 2023\.  Midjourney Terms of Service.  https://docs.midjourney.com/docs/terms-of-service
  * Mukundan et al. (2001) R. Mukundan, S.H. Ong, and P.A. Lee. 2001.  Image analysis by Tchebichef moments.  _IEEE Transactions on Image Processing_ 10, 9 (2001), 1357–1364.  https://doi.org/10.1109/83.941859
  * Niwattanakul et al. (2013) Suphakit Niwattanakul, Jatsada Singthongchai, Ekkachai Naenudorn, and Supachanun Wanapu. 2013\.  Using of Jaccard Coefficient for Keywords Similarity.
  * of Art ([n. d.]) The Metropolitan Museum of Art. [n. d.].  The Met: Open Access.  https://www.metmuseum.org/about-the-met/policies-and-documents/open-access#get-started-header
  * Office (2008) U.S. Copyright Office. 2008\.  Definitions (FAQ) — U.S. Copyright Office.  https://www.copyright.gov/help/faq/faq-definitions.html
  * Office (2022) U.S. Copyright Office. 2022\.  U.S. Copyright Office Fair Use Index.  https://www.copyright.gov/fair-use/
  * Ogwok and Ehlers (2022) Daniel Ogwok and Elizabeth Marie Ehlers. 2022.  Jaccard Index in Ensemble Image Segmentation: An Approach. In _The 5th International Conference on Computational Intelligence and Intelligent Systems (CIIS 2022))_.  https://doi.org/10.1145/3581792.3581794
  * Papineni et al. (2002) Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002\.  BLEU: a method for automatic evaluation of machine translation _(ACL ’02)_. Association for Computational Linguistics, USA, 8 pages.  https://doi.org/10.3115/1073083.1073135
  * Pilkington (2016) Marc Pilkington. 2016\.  Blockchain Technology: Principles and Applications.  In _Research Handbook on Digital Transformations_ , X. F. Olleros and M. Zhegu (Eds.). Edward Elgar Publishing, 225–253.  https://doi.org/10.4337/9781784717766.00019
  * Qazi et al. (2024) Zubair Qazi, William Shiao, and Evangelos E. Papalexakis. 2024\.  GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method.  (2024).  arXiv:2403.07321 [cs.CL]
  * Quach (2024) Katyanna Quach. 2024\.  Spreadsheet of “ripped off” artists lands in Midjourney case.  https://www.theregister.com/2024/01/04/midjourney_artists_spreadsheet/
  * Quang (2021) Jenny Quang. 2021\.  Does Training AI Violate Copyright Law.  _Berkeley Technology Law Journal_ (2021).
  * Qureshi and Megías Jiménez (2021) Amna Qureshi and David Megías Jiménez. 2021.  Blockchain-Based Multimedia Content Protection: Review and Open Challenges.  _Applied Sciences_ 11, 1 (2021).  https://doi.org/10.3390/app11010001
  * Quy et al. (2022) Tai Le Quy, Arjun Roy, Vasileios Iosifidis, Wenbin Zhang, and Eirini Ntoutsi. 2022.  A survey on datasets for fairness-aware machine learning.  _Data Mining and Knowledge Discovery_ (2022).
  * Ren et al. (2024a) Jie Ren, Han Xu, Pengfei He, Yingqian Cui, Shenglai Zeng, Jiankun Zhang, Hongzhi Wen, Jiayuan Ding, Hui Liu, Yi Chang, and Jiliang Tang. 2024a.  Copyright Protection in Generative AI: A Technical Perspective.  _arXiv_ (2024).  https://arxiv.org/abs/2402.02333
  * Ren et al. (2024b) Jie Ren, Han Xu, Pengfei He, Yingqian Cui, Shenglai Zeng, Jiankun Zhang, Hongzhi Wen, Jiayuan Ding, Hui Liu, Yi Chang, and Jiliang Tang. 2024b.  Copyright Protection in Generative AI: A Technical Perspective.  (2024).
  * Salkowitz (2022) Rob Salkowitz. 2022\.  Midjourney Founder David Holz On The Impact Of AI On Art, Imagination And The Creative Economy.  https://www.forbes.com/sites/robsalkowitz/2022/09/16/midjourney-founder-david-holz-on-the-impact-of-ai-on-art-imagination-and-the-creative-economy/?sh=1404a2e22d2b
  * Samuelson (2023) Pamela Samuelson. 2023\.  Generative AI meets copyright - science.  https://www.science.org/doi/10.1126/science.adi0656
  * Sanivarapu et al. (2022) Prasanth Vaidya Sanivarapu, Kandala N. V. P. S. Rajesh, Khalid M. Hosny, and Mostafa M. Fouda. 2022\.  Digital Watermarking System for Copyright Protection and Authentication of Images Using Cryptographic Techniques.  _Applied Sciences_ 12, 17 (Aug 2022), 1–13.  https://doi.org/10.3390/app12178724
  * Sarfraz et al. (2022) Fahad Sarfraz, Elahe Arani, and Bahram Zonooz. 2022\.  SYNERgy between SYNaptic consolidation and Experience Replay for general continual learning. In _1st Conference on Lifelong Learning Agents (CoLLAs 2022)_.  arXiv:2206.04016 [cs.NE]
  * Saxena et al. (2023) Nripsuta Ani Saxena, Wenbin Zhang, and Cyrus Shahabi. 2023\.  Missed Opportunities in Fair AI. In _Proceedings of the 2023 SIAM International Conference on Data Mining (SDM)_. SIAM, 961–964.
  * Scarciotti and Astolfi (2017) Giordano Scarciotti and Alessandro Astolfi. 2017.  Data-driven model reduction by moment matching for linear and nonlinear systems.  _Automatica_ 79 (2017), 340–351.  https://doi.org/10.1016/j.automatica.2017.01.014
  * Schmidhuber (2015) Jürgen Schmidhuber. 2015\.  Deep Learning in Neural networks: an Overview.  _Neural Networks_ 61, 61 (Jan 2015), 85–117.  https://doi.org/10.1016/j.neunet.2014.09.003
  * Scribbr ([n. d.]) Scribbr. [n. d.].  https://www.scribbr.com/plagiarism-checker/
  * Shan et al. (2023) Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, and Ben Y. Zhao. 2023\.  Glaze: protecting artists from style mimicry by text-to-image models. In _Proceedings of the 32nd USENIX Conference on Security Symposium_ (Anaheim, CA, USA) _(SEC ’23)_. USENIX Association, USA, Article 123, 18 pages.
  * Shiao (2024) William Shiao. 2024\.  madlab-ucr/GriD.  https://github.com/madlab-ucr/grid
  * Shivakumar and Garcia-Molina (1995) Narayanan Shivakumar and Hector Garcia-Molina. 1995.  SCAM: A Copy Detection Mechanism for Digital Documents.  _Stanford University InfoLab_ (Jan 1995).
  * Silva and Bird (2023) Ravidu Silva and Jordan J. Bird. 2023.  AI-ArtBench.  https://www.kaggle.com/datasets/ravidussilva/real-ai-art
  * Simonyan and Zisserman (2015) Karen Simonyan and Andrew Zisserman. 2015.  Very Deep Convolutional Networks for Large-Scale Image Recognition. In _3rd International Conference on Learning Representations (ICLR 2015)_ , Yoshua Bengio and Yann LeCun (Eds.).  http://arxiv.org/abs/1409.1556
  * Skodras (2003) Athanassios Skodras. 2003\.  _Discrete Wavelet Transform: an Introduction_.  1–26 pages.
  * Small (2023) Zachary Small. 2023\.  Sarah Silverman Sues OpenAI and Meta Over Copyright Infringement.  _The New York Times_ (Jul 2023).  https://www.nytimes.com/2023/07/10/arts/sarah-silverman-lawsuit-openai-meta.html
  * Stempel (2023) Jonathan Stempel. 2023\.  NY Times sues openai, Microsoft for infringing copyrighted works …  https://www.reuters.com/legal/transactional/ny-times-sues-openai-microsoft-infringing-copyrighted-work-2023-12-27/
  * Sun et al. (2023) Yanshen Sun, Jianfeng He, Shuo Lei, Limeng Cui, and Chang-Tien Lu. 2023.  Med-MMHL: a Multi-Modal Dataset for Detecting Human- and LLM-Generated Misinformation in the Medical Domain.  _arXiv_ (Jun 2023).  https://doi.org/10.48550/arxiv.2306.08871
  * Tan et al. (2019) Wei Ren Tan, Chee Seng Chan, Hernan Aguirre, and Kiyoshi Tanaka. 2019.  Improved ArtGAN for Conditional Synthesis of Natural Image and Artwork.  _IEEE Transactions on Image Processing_ 28, 1 (2019), 394–409.  https://doi.org/10.1109/TIP.2018.2866698
  * Tang et al. (2020) Xuejiao Tang, Liuhua Zhang, et al. 2020\.  Using machine learning to automate mammogram images analysis. In _IEEE International Conference on Bioinformatics and Biomedicine (BIBM)_. 757–764.
  * Tang et al. (2021) Xuejiao Tang, Wenbin Zhang, Yi Yu, Kea Turner, Tyler Derr, Mengyu Wang, and Eirini Ntoutsi. 2021.  Interpretable Visual Understanding with Cognitive Attention Network. In _International Conference on Artificial Neural Networks_. Springer, 555–568.
  * Team (2022) Wex Definitions Team. 2022\.  Infringement (of copyright).  https://www.law.cornell.edu/wex/infringement_(of_copyright)
  * Thompson (2024) Stuart A. Thompson. 2024\.  We Asked A.I. to Create the Joker. It Generated a Copyrighted Image.  _The New York Times_ (Jan 2024).  https://www.nytimes.com/interactive/2024/01/25/business/ai-image-generators-openai-microsoft-midjourney-copyright.html
  * TinEye ([n. d.]) TinEye. [n. d.].  https://tineye.com
  * Trappey et al. (2019) Amy J.C. Trappey, Charles V. Trappey, and Sam C.-C. Lin. 2019\.  _cover Detecting Trademark Image Infringement Using Convolutional Neural Networks_.  IOS Press Ebooks, 477–485.
  * Trappey et al. (2020) Charles V. Trappey, Amy J.C. Trappey, and Sam C.-C. Lin. 2020\.  Intelligent trademark similarity analysis of image, spelling, and phonetic features using machine learning methodologies.  _Advanced Engineering Informatics_ 45 (Aug 2020), 101–120.  https://doi.org/10.1016/j.aei.2020.101120
  * Wang et al. (2021a) Nana Wang, Hui Xu, Feng Xu, and Lei Cheng. 2021a.  The Algorithmic Composition for Music Copyright Protection Under Deep Learning and Blockchain.  Elsevier.  _Applied Soft Computing_ (2021).
  * Wang et al. (2020) Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A. Efros. 2020.  CNN-generated images are surprisingly easy to spot… for now.  _arXiv_ (2020).  http://arxiv.org/abs/1912.11035
  * Wang et al. (2021b) Xuejian Wang, Wenbin Zhang, Aishwarya Jadhav, and Jeremy Weiss. 2021b.  Harmonic-Mean Cox Models: A Ruler for Equal Attention to Risk. In _Survival Prediction-Algorithms, Challenges and Applications_. PMLR, 171–183.
  * Wang et al. (2023a) Zichong Wang, Giri Narasimhan, Xin Yao, and Wenbin Zhang. 2023a.  Mitigating multisource biases in graph neural networks via real counterfactual samples. In _2023 IEEE International Conference on Data Mining (ICDM)_. IEEE, 638–647.
  * Wang et al. (2023b) Zichong Wang, Nripsuta Saxena, Tongjia Yu, Sneha Karki, Tyler Zetty, Israat Haque, Shan Zhou, Dukka Kc, Ian Stockwell, Xuyu Wang, et al. 2023b.  Preventing discriminatory decision-making in evolving data streams. In _Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency_. 149–159.
  * Wang et al. (2023c) Zichong Wang, Charles Wallace, Albert Bifet, Xin Yao, and Wenbin Zhang. 2023c.  : Fairness-aware graph generative adversarial networks. In _Joint European Conference on Machine Learning and Knowledge Discovery in Databases_. Springer, 259–275.
  * Wang et al. (2023d) Zichong Wang, Yang Zhou, Meikang Qiu, Israat Haque, Laura Brown, Yi He, Jianwu Wang, David Lo, and Wenbin Zhang. 2023d.  Towards fair machine learning software: Understanding and addressing model bias through counterfactual thinking.  _arXiv preprint arXiv:2302.08018_ (2023).
  * Weaver (2018) John F Weaver. 2018\.  Everything Is Not Terminator: The Importance of Regulating Al As Soon As Possible.  _RAIL: The Journal of Robotics, Artificial Intelligence & Law_ (2018).
  * WikiArt (2022) WikiArt. 2022.  https://huggingface.co/datasets/huggan/wikiart
  * Xia et al. (2016) Wen Xia, Hong Jiang, Dan Feng, Fred Douglis, Philip Shilane, Yu Hua, Min Fu, Yucheng Zhang, and Yukun Zhou. 2016.  A Comprehensive Study of the Past, Present, and Future of Data Deduplication. In _Proceedings of the IEEE_ , Vol. 104. 1681–1710.  https://doi.org/10.1109/jproc.2016.2571298
  * Xu et al. (2023) Heng Xu, Tianqing Zhu, Lefeng Zhang, Waneli Zhou, and Phillip S. Yu. 2023.  Machine Unlearning: A Survey.  _Comput. Surveys_ (2023).
  * Yandex ([n. d.]) Yandex. [n. d.].  https://yandex.com/images/
  * Yazdani et al. (2024) Shamim Yazdani, Nripsuta Saxena, Zichong Wang, Yanzhao Wu, and Wenbin Zhang. 2024.  A Comprehensive Survey of Image and Video Generative AI: Recent Advances, Variants, and Applications.  (2024).
  * Young et al. (2014) Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. 2014\.  From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions.  _Transactions of the Association for Computational Linguistics_ 2 (2014), 67–78.  https://www.aclweb.org/anthology/Q14-1006.pdf
  * Zhang et al. (2018a) Daniel Zhang, Jose Badilla, Herman Tong, and Ben Zhong Tang. 2018a.  An End-to-End Scalable Copyright Detection System for Online Video Sharing Platforms.  _Advances in Social Networks Analysis and Mining_ (Aug 2018), 626–629.  https://doi.org/10.1109/asonam.2018.8508288
  * Zhang et al. (2018b) Daniel Yue Zhang, Qi Li, Herman Tong, Jose Badilla, Yang Zhang, and Dong Wang. 2018b.  Crowdsourcing-Based Copyright Infringement Detection in Live Video Streams. In _2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)_.  https://doi.org/10.1109/asonam.2018.8508523
  * Zhang et al. (2019b) Kevin A. Zhang, Lei Xu, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019b.  Robust Invisible Video Watermarking with Attention.  (Sep 2019).  https://arxiv.org/abs/1909.01285
  * Zhang et al. (2011) Lin Zhang, Lei Zhang, Xuanqin Mou, and David Zhang. 2011\.  FSIM: A Feature Similarity Index for Image Quality Assessment.  _IEEE Transactions on Image Processing_ 20, 8 (2011), 2378–2386.  https://doi.org/10.1109/TIP.2011.2109730
  * Zhang et al. (2021c) Mingli Zhang, Fan Zhang, Jianxin Zhang, Ahmad Chaddad, Fenghua Guo, Wenbin Zhang, Ji Zhang, and Alan Evans. 2021c.  Autoencoder for neuroimage. In _International conference on database and expert systems applications_. Springer, 84–90.
  * Zhang et al. (2020b) Mingli Zhang, Xin Zhao, et al. 2020b.  Deep discriminative learning for autism spectrum disorder classification. In _International Conference on Database and Expert Systems Applications_. Springer, 435–443.
  * Zhang et al. (2023d) Tiantian Zhang, Xueqian Wang, Bin Liang, and Bo Yuan. 2023d.  Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation.  _IEEE Transactions on Neural Networks and Learning Systems_ 34, 12 (Dec. 2023), 9925–9939.  https://doi.org/10.1109/tnnls.2022.3162241
  * Zhang (2017) Wenbin Zhang. 2017\.  Phd forum: Recognizing human posture from time-changing wearable sensor data streams. In _IEEE International Conference on Smart Computing (SMARTCOMP)_.
  * Zhang (2020) Wenbin Zhang. 2020\.  Learning fairness and graph deep generation in dynamic environments.  (2020).
  * Zhang (2024) Wenbin Zhang. 2024\.  Fairness with Censorship: Bridging the Gap between Fairness Research and Real-World Deployment. In _Proceedings of the AAAI Conference on Artificial Intelligence_ , Vol. 38. 22685–22685.
  * Zhang et al. (2020a) Wenbin Zhang et al. 2020a.  Flexible and adaptive fairness-aware learning in non-stationary data streams. In _IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)_. 399–406.
  * Zhang et al. (2021a) Wenbin Zhang, Albert Bifet, Xiangliang Zhang, Jeremy C Weiss, and Wolfgang Nejdl. 2021a.  FARF: A Fair and Adaptive Random Forests Classifier. In _Pacific-Asia Conference on Knowledge Discovery and Data Mining_. Springer, 245–256.
  * Zhang et al. (2023a) Wenbin Zhang, Tina Hernandez-Boussard, and Jeremy Weiss. 2023a.  Censored fairness through awareness. In _Proceedings of the AAAI conference on artificial intelligence_ , Vol. 37. 14611–14619.
  * Zhang and Ntoutsi (2019) Wenbin Zhang and Eirini Ntoutsi. 2019.  FAHT: an adaptive fairness-aware decision tree classifier. In _International Joint Conference on Artificial Intelligence (IJCAI)_. 1480–1486.
  * Zhang et al. (2022) Wenbin Zhang, Shimei Pan, Shuigeng Zhou, Toby Walsh, and Jeremy C Weiss. 2022.  Fairness Amidst Non-IID Graph Data: Current Achievements and Future Directions.  _arXiv preprint arXiv:2202.07170_ (2022).
  * Zhang et al. (2016) Wenbin Zhang, Jian Tang, and Nuo Wang. 2016.  Using the machine learning approach to predict patient survival from high-dimensional survival data. In _IEEE International Conference on Bioinformatics and Biomedicine (BIBM)_.
  * Zhang et al. (2019a) Wenbin Zhang, Xuejiao Tang, and Jianwu Wang. 2019a.  On fairness-aware learning for non-discriminative decision-making. In _International Conference on Data Mining Workshops (ICDMW)_. 1072–1079.
  * Zhang and Wang (2018) Wenbin Zhang and Jianwu Wang. 2018.  Content-bootstrapped collaborative filtering for medical article recommendations. In _IEEE International Conference on Bioinformatics and Biomedicine (BIBM)_.
  * Zhang et al. (2018c) Wenbin Zhang, Jianwu Wang, Daeho Jin, Lazaros Oreopoulos, and Zhibo Zhang. 2018c.  A deterministic self-organizing map approach and its application on satellite data based cloud type classification. In _IEEE International Conference on Big Data (Big Data)_.
  * Zhang et al. (2023c) Wenbin Zhang, Zichong Wang, Juyong Kim, Cheng Cheng, Thomas Oommen, Pradeep Ravikumar, and Jeremy Weiss. 2023c.  Individual fairness under uncertainty.  _arXiv preprint arXiv:2302.08015_ (2023).
  * Zhang and Weiss (2021) Wenbin Zhang and Jeremy Weiss. 2021.  Fair Decision-making Under Uncertainty. In _2021 IEEE International Conference on Data Mining (ICDM)_. IEEE.
  * Zhang and Weiss (2022) Wenbin Zhang and Jeremy C Weiss. 2022.  Longitudinal fairness with censorship. In _Proceedings of the AAAI Conference on Artificial Intelligence_ , Vol. 36. 12235–12243.
  * Zhang and Weiss (2023) Wenbin Zhang and Jeremy C Weiss. 2023.  Fairness with censorship and group constraints.  _Knowledge and Information Systems_ (2023), 1–24.
  * Zhang et al. (2021b) Wenbin Zhang, Liming Zhang, Dieter Pfoser, and Liang Zhao. 2021b.  Disentangled Dynamic Graph Deep Generation. In _Proceedings of the SIAM International Conference on Data Mining (SDM)_. 738–746.
  * Zhang and Zhao (2020) Wenbin Zhang and Liang Zhao. 2020.  Online decision trees with fairness.  _arXiv preprint arXiv:2010.08146_ (2020).
  * Zhang et al. (2023b) Yongjing Zhang, Zhaobo Lu, Feng Zhang, Hao Wang, and Shaojing Li. 2023b.  Machine Unlearning by Reversing the Continual Learning.  _Applied Sciences_ 13, 16 (Aug 2023), 1–10.  https://doi.org/10.3390/app13169341
  * Zhao et al. (2023) Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Ngai-Man Cheung, and Min Lin. 2023\.  A Recipe for Watermarking Diffusion Models.  (2023).
  * Zheng et al. (2021) Jingjing Zheng, Shuhua Teng, Peirong Li, Wei Ou, Donghao Zhou, and Jun Ye. 2021\.  A Novel Video Copyright Protection Scheme Based on Blockchain and Double Watermarking.  _Security and Communication Networks_ 2021 (Dec 2021), 1–16.  https://doi.org/10.1155/2021/6493306
  * Zirpoli (2023) Christopher T. Zirpoli. 2023\.  Generative Artificial Intelligence and Copyright Law.  Congressional Research Service.  LSB10922.