# Tackling GenAI Copyright Issues:
Originality Estimation and Genericization

Hiroaki Chiba-Okabe111Graduate Group in Applied Mathematics and Computational
Science, University of Pennsylvania, Philadelphia, PA 19104, USA. Weijie J.
Su222Department of Statistics and Data Science, Wharton School, University of
Pennsylvania, Philadelphia, PA 19104, USA.

###### Abstract

The rapid progress of generative AI technology has sparked significant
copyright concerns, leading to numerous lawsuits filed against AI developers.
While some studies explore methods to mitigate copyright risks by steering the
outputs of generative models away from those resembling copyrighted data,
little attention has been paid to the question of _how much_ of a resemblance
is undesirable; more original or unique data are afforded stronger protection,
and the threshold level of resemblance for constituting infringement
correspondingly lower. Here, leveraging this principle, we propose a
genericization method that modifies the outputs of a generative model to make
them more generic and less likely to infringe copyright. To achieve this, we
introduce a metric for quantifying the level of originality of data in a
manner that is consistent with the legal framework. This metric can be
practically estimated by drawing samples from a generative model, which is
then used for the genericization process. Experiments demonstrate that our
genericization method successfully modifies the output of a text-to-image
generative model so that it produces more generic, copyright-compliant images.

##  1 Introduction

Generative models have exhibited performance that rivals human capabilities in
creative tasks such as those involving image synthesis and language
processing.[1, 2] However, this progress has also heightened concerns over
copyright protection, leading to numerous lawsuits filed by creators against
the AI developers.[3] Copyright law aims to protect the rights of creators
over their works, encouraging the creation of new works while maintaining the
balance between the interests of creators and the public.[4] Concerns about
copyright issues with generative models arise primarily because these models
are trained on massive datasets that often contain copyrighted works and are
capable of producing outputs closely resembling their training data,
potentially resulting in violation of exclusive rights of copyright owners.[5]

To mitigate copyright concerns, previous studies have explored methods for
reducing the likelihood of producing outputs that resemble copyrighted
training data by modifying the training or inference of generative models.[6,
7, 8] However, little attention has been paid to the fact that the strength of
copyright protection afforded to different works is not uniform. Specifically,
works of higher originality receive stronger copyright protection than those
of lower originality. In court, this differential treatment materializes in
the varying threshold level of resemblance for finding infringement and the
application of doctrines limiting the protection of less original works.[9,
10, 11, 12, 13] The capacity of generative models to be trained on a massive
corpus of preexisting creations and learning the distribution of data can
potentially be leveraged to measure the originality of works.[14, 15, 13, 16]
In this way, generative models not only pose a threat to copyright protection,
but also offer means to answer a central question concerning copyright
protection of how original each work is, which, in turn, determines the
strength of copyright protection afforded to that work.

In this paper, we propose a genericization method to modify the output of
prompt-based generative models to be more generic, reducing the risks of
copyright infringement. It leverages a simple and interpretable metric for
quantifying originality that can be practically estimated by drawing samples
from a generative model. We apply our genericization method to a text-to-image
generative model to demonstrate that it successfully concentrates the outputs
of the generative model around more generic, copyright-compliant ones while
suppressing outputs that are highly similar to copyrighted images. Our
mitigation method capitalizes on the fact that generic works, as opposed to
highly original ones, receive only weak copyright protection, if any. It
requires only black-box access to the generative model and no modification to
the training process.

##  2 Originality estimation

#### Originality in the context of copyright law

AI developers and users might infringe copyright if generative AI produces
outputs that resemble existing works. Specifically, outputs that are
_substantially similar_ to original works can potentially infringe copyright
in them.[17] This means that infringement is found only when the level of
similarity reaches some threshold that it is considered substantial.
Importantly, the higher the level of _originality_ an work possesses, the
lower the level of resemblance required to constitute infringement, reflecting
the stronger copyright protection for that work.[10] In other words, a close
copy of a highly generic work may not be considered copyright infringement,
falling short of the substantial similarity threshold, while moderate
resemblance may be sufficient to infringe copyright in a highly original
work.[11, 10] The differential protection based on the level of originality
also materializes in various legal doctrines such as scènes à faire, which
denies copyright protection to works or parts of works that are highly typical
or commonly found within the specific genre to which the work belongs.[9, 13]

#### Originality metric

The concept of originality, upon which copyright law bases the strength of its
protection, can be decomposed into the likelihood of being distinct from other
works and the degree of the distinctiveness.[11, 18, 19] We propose the
following metric for quantifying originality, which captures this intuition.

$\begin{split}\mathsf{Originality}(c|x)&:=\int_{\mathcal{Y}}P(y|x)d(c,y)dy\\\
&=\mathbb{E}_{y\sim P(\cdot|x)}[d(c,y)]\end{split}$ (1)

$c$ is some fixed creation whose originality is to be quantified, $P(y|x)$ is
a probability distribution that reflects the distribution of creations in the
real world with some conditioning $x$, and $d$ is some distance metric.

The presence of $x$ reflects the fact that it is often appropriate in the
context of copyright law to evaluate originality with some baseline in mind.
For example, Mickey Mouse enjoys strong copyright protection because it is
highly distinct from ordinary and mundane depictions of a mouse that exist
abundantly in the world.[20, 11] This can be understood as Mickey Mouse having
a high originality value when measured using $P(y|\textrm{``a mouse"})$ as the
distribution of creations in Equation 1. Also, Batmobile is strongly protected
by copyright due to its distinctive characteristics, such as its bat-like
appearance and futuristic technologies, that separate it from an ordinary
car.333DC Comics v. Towle, 802 F.3d 1012 (9th Cir. 2015).

Aside from the likelihood and degree of being distinct from other works, as
captured in our metric, a common-sense notion of originality might encompass
such factors as ingenuity and artistic merit. However, it is generally
understood that the copyright law does not concern these qualities of the
work.444The intent of the congress behind the phrase “original works of
authorship,” an essential statutory requirement for a work to be protected
under copyright, was that “[t]his standard does not include requirements of
novelty, ingenuity, or esthetic merit, and there is no intention to enlarge
the standard of copyright protection to require them” (H.R. REP. NO. 94-1476,
at 51, 1976). Thus, these considerations should not be directly taken into
account when determining the strength of copyright protection.

#### Leveraging generative models to estimate originality

The true $P(y|x)$ is not generally available, but generative models trained to
learn the real-world data distribution can be used as an approximation in
practice. For example, to quantify the originality of an image, a text-to-
image generative model can be substituted as $P(y|x)$. In this case, $x$ might
be thought of as the prompt that sets the distribution of the model.

Observe that the metric is written in the form of expectation. Therefore,
although it is difficult to directly calculate Equation 1 in practice, we can
obtain the estimate

$\widehat{\mathsf{Originality}}(c|x)=\frac{1}{n}\sum_{i=1}^{n}d(c,y_{i}),$ (2)

where $y_{i}$ are samples drawn from the generative model. This is an unbiased
estimator and, by the laws of large numbers, it converges to the true value as
we increase the number of samples.

#### Choices of the distance metric

The distance metric $d$ involved in Equation 1 could be some quantity that is
inversely related to the similarity between the works. Assessment of
similarity in the context of copyright involves an intuitive judgment,[4] and
the myriads of standards and methods devised by courts to analyze the
substantial similarity requirement could partly be understood as tools to
provide explanations to ad hoc intuitive judgments.[21] Such intuition could
be captured by deep learning-based methods, which have been found to align
well with human perception.[22, 23]

In our experiments, we use the cosine distance in terms of CLIP [24] and
DINOv2 [25] feature embeddings. In equations, cosine distance is defined as
$d(a,b)=1-s(a,b)$ where $s$ is the cosine similarity defined as
$s(a,b)=\frac{v_{a}\cdot v_{b}}{\rVert v_{a}\rVert\rVert v_{b}\rVert}$ with
$v_{a},v_{b}$ being the feature embeddings of $a$ and $b$. Our framework
allows for the flexibility of choosing the appropriate distance metric
depending on the application.

##  3 Genericization

Outputs of generative models can be genericized in a model-agnostic way by
internally producing $n$ samples, estimating the originality of each sample,
and only outputting the one that has the lowest estimated originality. For
computational efficiency, the originality estimate of a sample can be cross-
computed using other internally produced samples. In other words, the
genericization method internally produces $y_{i},i=1,2,\cdots,n$ and outputs
$y_{\mathrm{generic}}$ such that

$y_{\mathrm{generic}}=\underset{y_{i}}{\mathrm{argmin}}\,\frac{1}{n-1}\sum_{j\neq
i}d(y_{i},y_{j}).$ (3)

The resulting output is expected to be generic, as opposed to highly unique or
original, relative to the prompt. Genericization of output in this manner is
particularly suitable for addressing copyright concerns that arise in
situations where generative models somewhat accidentally produce outputs that
are highly similar to unique copyrighted images even though the prompt appears
harmless. [26] As long as the prompt describes an object that is sufficiently
generic, works that are highly similar to the genericized output will likely
be generic and thus receive only weak copyright protection, if any, which
requires near-exact copying to constitute infringement.

The likelihood of copyright infringement can be further minimized by combining
our genericization method with other methods for reducing the likelihood of
producing copyright-infringing content such as content filtering[5, 27] and
deduplication.[28, 29, 30]

##  4 Results

We use SDXL,[31] a text-to-image generative model, to obtain originality
estimates (Equation 2) of two images of copyrighted characters, Winnie the
Pooh and Mario from Super Mario Bros. We used five different prompts (Table 1)
for each image as the conditioning $x$, designed to describe the characters at
different levels of abstractness or specificity. The specific prompts were
generated by ChatGPT and other prompts were formed by excerpting parts of
these generated prompts. We also apply our genericization method to the
outputs of the generative model produced with the specific prompts in Table 1
and evaluate the performance.

|  Mario |  Pooh
---|---|---
Abstract |  “a mustachioed character” |  “a bear”
Intermediate |  “a plump, mustachioed character dressed in brown shoes” “a plump, mustachioed character dressed in blue overalls” “a mustachioed character dressed in brown shoes and white gloves” |  “a chubby, golden-yellow bear” “a chubby bear with a red shirt” “a golden-yellow bear with a red shirt”
Specific |  “a plump, mustachioed character dressed in a red hat and shirt, blue overalls, brown shoes, and white gloves” |  “a chubby, golden-yellow bear with a red shirt”
Table 1: List of prompts used in experiments. Five prompts with different
levels of abstraction were prepared for each character. Each prompt was fed
into the generative model to generate images which were used to estimate
originality scores.

Images of copyrighted characters as well as the sample images produced by the
generative model using each prompt are displayed in Figure 1. More specific
prompts tend to produce images that share more visual elements with the
copyrighted characters. See Supplementary Information for further details of
the experimental settings.

Figure 1: Copyrighted images and images generated by the generative model. The
leftmost column shows the copyrighted images of which originality values were
computed. The subsequent columns display sample images generated by the
generative model, starting from those generated by the very abstract prompts
on the left and moving towards those generated with increasingly specific
prompts towards the right.

#### Originality estimation

Our metric successfully assigns higher originality values when the generative
model is given more abstract prompts and lower originality values with more
specific prompts. This reflects the fact that typical generation with more
specific prompts, which more closely describe the copyrighted images, is more
likely to be highly similar to copyrighted images. In addition, originality
estimates for images of copyrighted characters tend to be higher than typical
output from the generative model, especially when more abstract prompts are
used (Figure 2). However, our originality estimation method can fail when the
distribution learned by the generative model is highly distorted (see
Supplementary Infromation for a case of failure).

Figure 2: Originality estimates of copyrighted images and generated images.
Estimated originality values of the copyrighted images (green bars) are higher
when more abstract prompts are used. Comparison with the mean and standard
deviation of originality estimates of images produced by the generative model
using each prompt (red dots and dotted lines) indicates that the originality
of the copyrighted images tends to be substantially higher than typical
outputs.

#### Genericization

The samples we obtain after genericization are expected to concentrate more
around generic data instead of unique ones. Indeed, when we plot the
distribution of similarity values between the images of copyrighted characters
and all internally produced samples along with those between the images of
copyrighted characters and $y_{\textrm{generic}}$ samples, it is apparent that
internally produced samples with high similarity values are suppressed by
genericization, and the distribution concentrates around intermediate
similarity values. In contrast, similarity between a generic image (here, we
pick the $y_{\textrm{generic}}$ sample with the lowest value of estimated
originality among all $y_{\textrm{generic}}$ samples) and genericized samples
tend to concentrate on higher values (Figure 3).

Figure 3: Distribution of similarity before and after genericization. Outputs
concentrate more around images highly similar to generic images after
genericization whereas outputs highly similar to the images of copyrighted
characters are suppressed.

Samples $y_{i}$ with the lowest estimated originality among those selected as
$y_{\textrm{generic}}$ as well as samples $y_{i}$ that are the most similar to
the copyrighted images are displayed in Figure 4. All five samples (for each
specific prompt) that are the most similar (in other words, have the highest
cosine similarity values) to the copyrighted images are not
$y_{\textrm{generic}}$, meaning that they were produced internally by the
model but not selected as the output. The five samples that are most similar
to the copyrighted image of Mario among all samples produced using the prompt
“a plump, mustachioed character dressed in a red hat and shirt, blue overalls,
brown shoes, and white gloves” share some distinctive features (e.g. short
statute, large round blue eyes, and a round emblem on the front of the cap, a
3D graphic style rendering) with Mario, and those that are most similar to the
copyrighted image of Pooh among all samples produced using the prompt “a
chubby, golden-yellow bear with a red shirt” tend to share some distinctive
features (e.g. a less pronounced snout and simplified, human-like facial
features) with Pooh. This qualitative observation supports our quantitative
results, suggesting that our genericization method effectively suppresses
outputs similar to unique copyrighted images in a way that aligns well with
human perception.

Figure 4: Generated images with low originality and high similarity to
copyrighted characters. The top row shows five images each that have the
lowest estimated originality among $y_{\textrm{generic}}$ generated using the
prompt “a plump, mustachioed character dressed in a red hat and shirt, blue
overalls, brown shoes, and white gloves” and those generated using the prompt
“a chubby, golden-yellow bear with a red shirt,” respectively. The bottom row
displays five images each that have the highest similarity to the images of
Mario and Pooh, respectively, all of which were suppressed by applying the
genericization method.

##  5 Related works

#### Quantification of copyright protection

Researchers have recently explored the application of computational methods to
copyright legal issues. For example, a framework for testing substantial
similarity based on Kolmogorov-Levin complexity has been proposed.[32] The
potential of machine learning to bring objectivity to the substantial
similarity test has also been recognized in the legal literature.[14] While a
deep-learning-based method for measuring creativity has been suggested,[15]
such methods can only be effective for our purposes if they align with the
framework of copyright law. More recently, the ability of generative models to
capture the distribution of expressions in a massive dataset has been
considered promising for measuring originality.[13, 16] In this context, a
method involving text inversion and reconstructing the original image from the
resulting text tokens has been discussed as a means to measure the originality
of images.[13] This paper contributes to the literature by introducing a
general and interpretable mathematical formula for quantifying the level of
originality, which is consistent with the legal framework and can be
practically estimated using generative models.

#### Mitigation of copyright concerns of generative models

Previous studies have explored methods for reducing the likelihood of
producing outputs that resemble copyrighted data by training an auxiliary
generative model on non-copyrighted data and employing rejection sampling,[6]
modifying the training objectives of generative models,[7] or introducing
adversarial perturbations to prevent mimicry of artist styles.[8] However, the
question of how much of a resemblance is undesirable, which is determined by
the level of originality of each work, has been overlooked in these studies.
As an alternative approach for mitigating the risks of infringing copyright in
training data, methods for computing fair shares of royalty and compensating
the authors of training data have been proposed, where the shares are computed
based on the contribution of copyrighted data to the likelihood of
generation.[33, 34] Such an approach might not be realistic when the
generative model is trained on massive data scraped from the web, which is
often the case.

##  6 Discussion

We introduced a method to genericize the output of generative models, thereby
reducing the risk of copyright infringement. Our method leverages the
principle that the level of originality of works determines the strength of
their copyright protection, as well as the capability of generative models to
quantify originality. Experiments demonstrate that our genericization method
effectively steers the outputs away from those highly similar to unique
copyrighted data and produces more generic, copyright-compliant ones.

In addition to designing more copyright-compliant generative models, our
originality metric can also potentially be used to provide supporting
materials in courts to decide on copyright infringement cases. Judges and
juries can refer to the value estimated by leveraging generative AI that may
support their judgment, potentially bringing objectivity and transparency.

Our work is not without limitations. While the general framework of
originality estimation and genericization is broadly applicable, our
experiments have focused on a specific text-to-image generative model and two
particular distance metrics. Future research needs to explore appropriate
choices of generative models and distance metrics for various types of works.
Additionally, systematic methods for setting thresholds to establish copyright
infringement based on originality estimates obtained using our framework
should be investigated.

### Acknowledgements

The authors acknowledge financial support from the Simons Foundation Math+X
Grant to the University of Pennsylvania and Wharton AI for Business.

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## Supplementary Information

###  S1 A case of failure

When an alternative set of prompts (Table S1) is used, originality estimates
are higher with the abstract prompt, “an Italian plumber,” than with more
specific prompts that contain finer details of the visual elements of Mario,
and the former estimates tend to be lower than the estimated originality of
typical outputs produced by the generative model (Figure S1). This is likely
because the prompt “an Italian plumber” is strongly associated with Mario, and
tends to produce images that resemble Mario rather than more specific prompts
(Figure S2).

Abstract |  “an Italian plumber”
---|---
Intermediate |  “a plump Italian plumber with a red shirt” “an Italian plumber with a red hat and brown shoes” “an Italian plumber with blue overalls and brown shoes”
Specific |  “a plump Italian plumber with a red hat and mustache, wearing blue overalls, a red shirt, and brown shoes”
Table S1: Alternative prompts used in the experiment. An alternative set of
five prompts with different levels of abstraction were prepared. Each prompt
was fed into the generative model to generate images which were used to
estimate originality values. Figure S1: Originality estimated by using CLIP as
the distance metric. When the abstract prompt “an Italian plumber” is used,
originality estimates of the copyrighted images tend to be lower than when
more specific prompts are used. The former estimates are also lower than those
of typical outputs from the generative model produced using the same prompt.
Figure S2: Images generated using the alternative prompts. The leftmost image
is generated with the very abstract prompt, with each subsequent image to the
right produced with increasingly specific prompts.

###  S2 Experimental settings

#### Image generation and pre-processing

We employed SDXL [31] to generate images of $1024$ by $1024$ pixels. To focus
our analysis on the main characters depicted in the images for easier
interpretability of results, we removed the background of all generated images
using rembg, a standard background removal tool.

#### Originality estimation

Originality values of copyrighted images drawn from the generative model for
each prompt $x$ were estimated according to Equation 2 using $40$ samples
(i.e. $n=40$). For each combination of a copyrighted image and a prompt, $40$
such estimates were obtained to calculate the mean and standard deviation of
the originality estimates of the copyrighted images when conditioned by the
prompt (green bars and black whiskers in Figures 2 and S1).

For each prompt, we also generated another $40$ samples whose originality
estimates were obtained in the same way as the originality estimates of the
copyrighted images are calculated. These estimates were used to compute the
mean and standard deviation of the originality estimates of outputs from the
generative model (red dots and dotted lines in Figures 2 and S1).

#### Genericization

$250$ samples of $y_{\textrm{generic}}$ were obtained for each of the two
specific prompt in Table 1, whose distance from the copyrighted and generic
images were computed to produce the orange bars in Figure 3. Each
$y_{\textrm{generic}}$ were selected among $40$ samples produced from the
generative model according to Equation 3. This amounts to a total of $10,000$
samples drawn from the generative model for each prompt, whose distance from
the copyrighted and generic images were computed to produce the blue bars in
Figure 3.