# Text-image guided Diffusion Model for generating Deepfake celebrity
interactions

Yunzhuo Chen  The University of Western Australia
Perth, Australia
<EMAIL_ADDRESS>Nur Al Hasan Haldar Ajmal Mian  The
University of Western Australia
Perth, Australia
<EMAIL_ADDRESS>The University of Western Australia
Perth, Australia
<EMAIL_ADDRESS>Naveed Akhtar
The University of Western Australia
Perth, Australia
<EMAIL_ADDRESS>
###### Abstract

Deepfake images are fast becoming a serious concern due to their realism.
Diffusion models have recently demonstrated highly realistic visual content
generation, which makes them an excellent potential tool for Deepfake
generation. To curb their exploitation for Deepfakes, it is imperative to
first explore the extent to which diffusion models can be used to generate
realistic content that is controllable with convenient prompts. This paper
devises and explores a novel method in that regard. Our technique alters the
popular stable diffusion model to generate a controllable high-quality
Deepfake image with text and image prompts. In addition, the original stable
model lacks severely in generating quality images that contain multiple
persons. The modified diffusion model is able to address this problem, it add
input anchor image’s latent at the beginning of inferencing rather than
Gaussian random latent as input. Hence, we focus on generating forged content
for celebrity interactions, which may be used to spread rumors. We also apply
Dreambooth to enhance the realism of our fake images. Dreambooth trains the
pairing of center words and specific features to produce more refined and
personalized output images. Our results show that with the devised scheme, it
is possible to create fake visual content with alarming realism, such that the
content can serve as believable evidence of meetings between powerful
political figures.

###### Index Terms:

Deepfake, text-to-image, diffusion model

##  I Introduction

Deepfake [29] face-forgery technology first appeared on the community forum
Reddit [18]. Since then, it has set off a wave of creating fake content [1].
The advancement of Deepfake generative models [11, 27, 13, 16, 17, 31, 15, 26]
can easily misguide human visual system. Deepfake technology, despite its
potential benefits in the field of entertainment, also raises serious concerns
for its potential negative societal impact. The ease of access to software and
codes for creating fake videos and images has resulted in a widespread abuse
of this technology, leading to legal violations such as creating illegal or
offensive content [18]. Therefore, it is crucial to address and mitigate the
negative consequences of its abuse. Currently, Deepfake content is generally
still human-identifiable. This is because existing Deepfake generation
processes usually focus on specific facial regions, which introduces
discernible artifacts in the overall content. However, recent advances in
computer vision have seen a quantum leap in the quality of visual content
generation through generative models, such as diffusion models [19, 30, 22, 8,
6]. This makes these models a potent potential tool for Deepfake generation.

Figure 1: Two-stream input U-Net consisting of (A) User input prompt; (B) User
input image. The utilization of an anchor image offers a significant advantage
in mitigating the uncertainty introduced by Gaussian noise. The anchor image
serves as a valuable reference point, enabling the model to generate images
that align closely with the desired characteristics and features depicted in
the anchor image. This helps to minimize the inherent uncertainty and enhances
the overall quality and accuracy of the generated content.

To curb the negative impact of Deepfake, it is imperative to thoroughly
explore the capacity of diffusion models [23, 25, 13, 17] as a tool to
generate controlled fake content. To that end, we focus on the widely popular
publicly accessible stable diffusion model [19], which has recently gained a
strong reputation for its generative abilities. We find that whereas stable
diffusion generates high-quality synthetic content, its off-the-shelf use for
controlled and topic-oriented fake content generation still requires some
effort. The random seed used by the model in the generation process is the
main reason for this phenomenon. The generated images suffer from
uncontrollable background and disfigured foreground when off-the-shelf model
is used to generate scenes with multiple characters. Especially, the forged
handshake details are easily identifiable, even in the good quality generated
samples.

In this work, we explore ways to significantly improve the quality of the
visual content generated by the stable diffusion model [19] by extending the
original design, such that the generated images can serve as believable
evidence for significant events, e.g., meetings between powerful political
figures. In particular, our approach is able to provide convenient text and
image handles over the content which enables controlling the background and
foreground content. Our proposed Text-Image Guided Diffusion Model (TIDM)
leverages an input image as guidance to improve the content quality of the
generated images, which also enables generating consistent multiple images of
an event, and better meet the needs of the Deepfake task. To prevent output
randomness, our approach incorporates an anchor image and text into the
model’s input. Additionally, we introduce a novel technique, inspired by
Dreambooth [21], to streamline the training of the modified model, ensuring
convenience and efficiency.

We conducted comprehensive experiments using four different metrics to
evaluate our approach. Through meticulous experimentation and ablation
studies, we demonstrate the efficacy of our approach. Our findings underscore
the potential risks associated with using generative modeling to create
convincing fake content as evidence of influential events. They also emphasize
the importance of implementing practical measures to mitigate the adverse
impacts of Deepfake on society. The contributions of this paper can be
summarized as follows.

  * •

This work addresses the Deepfake generation problem from a diffusion model
perspective, considering both characters and backgrounds in the Deepfake
generation task as a whole.

  * •

To reduce generative randomness in the diffusion model, we enhance the
underlying traditional U-Net architecture by incorporating not only text but
also the latent representation of an anchor image related to the generated
content.

  * •

By leveraging Dreambooth technique, we propose an efficient training method
for our proposed model.

  * •

We validate the effectiveness of our technique through extensive experiments
and ablation studies.

Figure 2: The original stable diffusion model [19] consists of three modules:
Text Encoder, Image Information Creator, and Image Decoder. Our technique
introduces an additional Image Encoder that allows leveraging an anchor image.
The proposed model uses a two-stream denoising U-Net to process the additional
data modality in the Image Information Creator block. Our model produces high-
quality realistic image(s) that can depict interactive event, e.g., meetings,
with consistent multiple forged images.

##  II RELATED WORK

The rapid development and exploration of robust and powerful Deepfake
generation methods are essential to address the increasing security concerns
arising from forged visual content. Numerous deepfake generation techniques
employing generative adversarial networks (GANs) have been extensively studied
in the literature to investigate the security risks associated with generative
visual modeling [11, 27, 13, 16, 17, 31, 15, 26]. Early research in this
domain predominantly focused on constructing Deepfake generative networks
using neural networks. However, the advancement of generative models in the
deep learning field has been comparatively slower than that of discriminative
models. The introduction of Generative Adversarial Networks (GANs) by
Goodfellow et al. [5] has significantly enhanced the capabilities of
generative models, revolutionizing the field of generative visual modeling.

ProGAN [9] and StyleGAN [10] techniques have achieved remarkable success in
generating high-resolution images with desired features. These methods, along
with further research on novel architectures and their application, hold
significant potential for advancing Deepfake generation. However, existing
Deepfake generation methods still face certain limitations. One limitation
involves the lack of realistic backgrounds in the generated images [31, 15].
Blurring the background to focus on the face can result in distribution
differences, as highlighted in studies such as [10]. Moreover, most face
forgery techniques only modify specific regions of the face, which often
involves operations like cropping and splicing. These operations introduce
counterfeit traces and artifacts. For instance, the process of blurring the
face contour using Gaussian noise can introduce distribution differences.
Additionally, the stitching of images may lead to discrepancies in
distribution compared to real images. Chen et al. [3] demonstrated that
splicing and cropping can create differences in image artifacts when compared
to authentic images. These limitations highlight the need for further research
and advancements in Deepfake generation to address issues related to realistic
backgrounds, distribution differences, and counterfeit traces resulting from
localized modifications and image operations.

Diffusion models [19, 7, 4] have received considerable attention recently due
to their high quality visual content generation. These models also do not need
to align the posterior distribution like VAE [12], or train additional
discriminators like GANs [5]. They aim to transform data distribution into a
tractable probability distribution, and then learn the reverse process in a
step-by-step manner by parameterzing the reverse kernel with neural models.
Though the generative abilities of diffusion models are remarkable, their
sampling process has intrinsic randomness [2]. This leads to a lack of control
over the generated visual content. We find that generally, even detailed
prompts fail to reasonably control the content of the generated images. This
makes forgery of topic-oriented content with diffusion models problematic.
Moreover, the current models also lack in generating high-quality images of
events involving interactions between multiple people. Our technique
particularly focuses on these aspects. By guiding the image generation process
with an anchor image, we provide consistency between multiple images that can
collectively depict a forged event. We also improve the quality of the
generated content that involves interaction between multiple people.

Figure 3: The U-Net serves as the key component for generating images from
noise. During the prediction process, the UNET is invoked multiple times,
where each invocation removes the noise slice output by the previous UNET
prediction from the original noise, gradually obtaining a denoised image
representation. The UNET utilized in the SDM consists of approximately 860
million parameters and requires around 3.4 gigabytes of storage space when
encoded with float32 precision.

In the past year, most popular diffusion models have utilized the
convolutional U-Net [20] as their backbone. The U-Net architecture involves
changing the dimensions of latent variables through a combination of
downsampling and upsampling operations, as illustrated in Fig. 3. The entire
model can be understood as a sequence of denoising autoencoders with equal
weights. To fully harness the potential of U-Net, we have modified its
framework to accept both tokenized text and image inputs. To further improve
the performance of the model, we have leveraged the fine-tuning capabilities
of DreamBooth, a deep-learning generation model designed to enhance text-to-
image models. By utilizing a real-person image as a reference, our objective
is to train the default model to generate specialized individuals or objects.
Furthermore, we have evaluated the stability of the image-to-image function
within the diffusion process. This evaluation involves measuring the
difference between the generated images and the original images using a
denoising strength parameter. When the denoising strength is set to 0, the
generated image will be an exact replica of the input image. However, as the
denoising strength increases towards 1, the generated image will become
increasingly distinct from the original image.

In summary, existing face forgery techniques suffer from blurring issues in
both the generated face area and the non-face areas, particularly in the
background of the image. Additionally, the randomness present in the
generation process of current diffusion models does not align with the
requirements of Deepfake generation tasks. The objective of our work is to
address these challenges by combining the strengths of Deepfake generative
models and Text-to-Image generative models, resulting in a novel image
generation technique. Our approach aims to overcome the blurred area problem
by introducing an input image that encompasses the entire scene. By
considering the person and the background as a unified entity in the
generative task, we effectively reduce counterfeit traces introduced through
operations such as image cropping and splicing. Furthermore, this approach
enhances the authenticity of the picture background. To enable the diffusion
model to generate more specific and targeted images, we have modified the
internal structure of the traditional U-Net. The new U-Net not only takes text
as input but also incorporates a latent representation of a related image in
the generation process. Moreover, unlike most diffusion models that focus on a
single object during generation, our proposed model caters to scenarios
involving multiple generated targets. It leverages a latent anchor image as
guidance to improve the content of the generated images, thereby better
meeting the requirements of Deepfake generation tasks.

##  III PROPOSED METHOD

We propose Text-image guided Diffusion Model (TIDM), a novel technique aimed
at generating realistic and consistent images for events such as meetings. The
generation of high-quality images conditioned on text inputs remains a
challenging task in the field of text-to-image synthesis. In this work, we
address the limitations of existing methods, particularly the popular Stable
Diffusion Model (SDM) [19], which is specifically designed for text-to-image
generation tasks. SDM is based on diffusion process, which employs an
iterative process of “denoising” data in a latent representation space and
subsequently decoding it to form complete images. However, while SDM has shown
promising results, it still faces certain challenges, as discussed in Section
II, which limit its ability to generate fake content for events like meetings.
To overcome these challenges, we introduce the Text-image guided Diffusion
Model (TIDM). Our proposed method leverages the strengths of SDM while
incorporating additional improvements. In TIDM, we enhance the generation
accuracy by introducing a new anchor image as a guiding reference in the U-Net
architecture. This anchor image provides additional contextual information
during the generation process, leading to improved image synthesis results.

We also develop a Dreambooth method based technique [21] for fine-tuning the
TIDM model. Dreambooth enables targeted adjustments to the generated images,
further enhancing their quality and coherence. By integrating dreambooth into
TIDM, we improve the fine-grained details and overall realism of the generated
images. To facilitate the generation process, TIDM utilizes the Contrastive
Language-Image Pre-training (CLIP)[14] to convert input text into a text
embedding. This text embedding is then fed into the denoising module known as
the Text-conditioned Latent U-Net[20]. The Text-conditioned Latent U-Net
processes the text embedding and outputs high-resolution images with
dimensions of 512x512 pixels. Our proposed TIDM technique overcomes the
limitations of SDM by incorporating the anchor image as a guiding reference
and fine-tuning through the dreambooth method. These improvements lead to
enhanced image synthesis performance, ensuring that the generated images
accurately correspond to the input text descriptions.

Figure 4: TIDM is our enhanced denoising model that incorporates additional
yellow blocks to handle both the image and text prompt representations in the
Image Information Creator of SDM-based pipeline. By utilizing a two-stream
architecture, TIDM effectively processes and integrates information from both
modalities to generate denoised outputs. The inclusion of the yellow blocks
enhances the model’s capability to effectively denoise images while
considering the accompanying text prompts, resulting in improved overall
performance.

TIDM introduces several modifications to address the limitations of SDM [19]
as a source for DeepFake generation, particularly when used to support rumors
with fake evidence. The framework of TIDM is illustrated in Fig. 2. By
incorporating an additional Image Encoder module, TIDM enables image guidance
to the SDM backbone, resulting in improved image generation. The Image Encoder
module in TIDM computes a latent representation for an anchor image, which
serves as a visual reference to guide the generation of fake images. This
latent representation is further processed by the Image Information Creator
module, which also takes into account the text prompt for the fake content.
Unlike SDM, our Image Information Creator has the unique ability to
simultaneously process both the text prompt and the anchor image. To achieve
this, we employ a two-stream denoising U-Net model, as depicted in Fig. 4.
Overall, the introduction of the anchor image and the utilization of the two-
stream denoising U-Net model in TIDM significantly enhance the quality of
generated images. These modifications enable TIDM to produce more convincing
and realistic DeepFake images by incorporating relevant visual guidance during
the generation process.

To mitigate the problems of blurred backgrounds and ensure a coherent
representation of both the person and the background in the generative task,
we adopt an approach that treats them as a unified entity. By considering the
person and the background together, we reduce the likelihood of introducing
artifacts that may arise from operations like cropping and splicing. This
approach enhances the overall realism of the generated background, resulting
in more visually convincing output. In Section III-A, we describe our improved
denoising process for the SDM to enable our two-stream U-Net, while Section
III-B explains how we utilized Dreambooth [21] to further improve the output
quality.

###  III-A Objective Functions

The overall learning objective of diffusion models can be expressed as
minimization of the following loss [7]

$L_{DM}=\mathbb{E}_{x,\epsilon\sim\mathcal{N}(0,1),t}\left\|\epsilon-\epsilon_{\theta}\left(x_{t},t\right)\right\|_{2}^{2},$
(1)

where, $t$ is sampled from ${1,2,\dots,T}$; $x_{t}$ is a noisy image of size
defined by the user, and $\epsilon$ and $\epsilon_{\theta}$ denote the
standard normal distribution samples and denoising autoencoder outputs,
respectively. Latent diffusion modeling operates in the latent space to reduce
computational complexity of the learning objects, and employs the following
loss.

$L_{LDM}:=\mathbb{E}_{\mathcal{E}(x),\epsilon\sim\mathcal{N}(0,1),t}\left\|\epsilon-\epsilon_{\theta}\left(z_{t},t\right)\right\|_{2}^{2}.$
(2)

In above, the latent representation $z_{t}$ can be efficiently obtained from
the encoder $\mathcal{E}$ during training, and samples from $p(z)$ can be
decoded to the image space with a single pass through a decoder $\mathcal{D}$.

Our network architecture features an input latent representation that also
incorporates the latent of an anchor image from the start. This representation
is computed by an image encoder, which is implemented in our technique using
the Huggingface Diffusers library [28]. Our image encoder uses down_blocks,
mid_block, and up_blocks. We further enhance the encoder by incorporating a
specialized PyTorch ModuleList that contains CrossAttnDownBlock2D and
ResnetBlock. TIDM’s most important ability is to simultaneously process two-
stream input of text and anchor image. To enable that, we must improve upon
the denoising process of the SDM technique. We achieve that with a conditional
denoising autoencoder, thereby effectively altering the loss function in Eq.
(2) to the following:

$L_{LDM}:=\mathbb{E}_{\mathcal{E}(x),\epsilon\sim\mathcal{N}(0,1),t}\left\|\epsilon-\epsilon_{\theta}^{*}\left(z_{t},t\right)\right\|_{2}^{2},$
(3)

where $\epsilon_{\theta}^{*}$ denotes the conditional denoising autoencoder
output. Our U-Net based architecture for the denoising process is shown in
Fig. 4. Note that, this architecture is an enhancement of the existing
architecture that is known to work well for the diffusion model [19], but was
previously incapable of processing both text and visual input.

In the original Stable Diffusion Model (SDM), the user input is limited to
text content and inference hyper-parameters, which determines the content of
the generated images. However, this loose control over the output often leads
to inconsistent and visually inferior results. To address these limitations,
we propose the Text-image guided Diffusion Model (TIDM), which introduces a
control mechanism using an anchor image for significant improvements.
Generating realistic faces and body part shapes becomes challenging for
traditional diffusion models, such as SDM, when the text prompts involve
multiple personalities. To address this limitation, our approach overcomes the
challenge by incorporating an anchor image. This incorporation enables us to
have more precise control over the generated images, enhancing their quality
and realism. To achieve this, we easily access celebrity images from the
Internet and utilize them as anchors in our generation process. By
incorporating the anchor image, TIDM ensures that the generated images exhibit
realistic face and body shapes, even when the text prompts involve multiple
personalities.

In addition to controlling the background, TIDM also focuses on generating
high-quality foreground personnel. To achieve this, we employ a modified
Dreambooth training method. Dreambooth allows targeted adjustments to the
generated images, enhancing their quality and coherence. By integrating this
method into TIDM, we are able to generate high-quality figures of the
foreground personnel, ensuring visually appealing and convincing results. The
combination of the anchor image control mechanism and the modified Dreambooth
training method enables TIDM to generate images with improved consistency,
visual quality, and realism. The use of an anchor image provides precise
guidance for the generation process, enhancing the coherence between multiple
output samples. Furthermore, the modified Dreambooth training method fine-
tunes the generated images, resulting in visually appealing foreground
personnel. By addressing the limitations of traditional diffusion models and
incorporating control mechanisms with anchor images and modified training
methods, TIDM offers a significant improvement in generating realistic and
visually consistent DeepFake images. The ability to control both the
background and foreground personnel enhances the quality and believability of
the generated images, making them more suitable for various applications in
the realm of DeepFake synthesis.

###  III-B Dreambooth Training Method

Our objective is to generate detailed and diverse images of a subject using
only a limited number of their casually obtained photos (3-5). To that end,
the first task is to implant the subject instance into the output domain of
the model and to bind the subject with a unique identifier. The identifier can
be varied depending on different objects users are interested in. We present
our method to design the identifier below, as well as our approach to
supervising the fine-tuning process of the model such that it can re-use its
prior learning and adapt it to subject instance. The Prior-Preservation Loss
used by our technique under the Dreambooth framework is given below.

$\begin{split}\mathbb{E}_{\mathbf{x},\mathbf{c},\boldsymbol{\epsilon},\boldsymbol{\epsilon}^{\prime},t}[w_{t}\left\|\hat{\mathbf{x}}_{\theta}\left(\alpha_{t}\mathbf{x}+\sigma_{t}\boldsymbol{\epsilon},\mathbf{c}\right)-\mathbf{x}\right\|_{2}^{2}+\\\
\lambda
w_{t^{\prime}}\left\|\hat{\mathbf{x}}_{\theta}\left(\alpha_{t^{\prime}}\mathbf{x}_{\mathrm{pr}}+\sigma_{t^{\prime}}\epsilon^{\prime},\mathbf{c}_{\mathrm{pr}}\right)-\mathbf{x}_{\mathrm{pr}}\right\|_{2}^{2}].\end{split}$
(4)

Here, $\lambda$ controls the prior-preservation term weight, $x$ represents
the ground-truth image, $c$ is a conditioning vector obtained from a text
prompt, $\epsilon$ is Gaussian noise distribution, and
$\alpha_{t},\sigma_{t},w_{t}$ are hyperparameters to regulate the noise
schedule and sample quality. To match our model’s input, we adapted the
Dreambooth training process for output size.

The task of Dreambooth is to obtain a unique model by training the base model.
The base model is obtained by stable diffusion, which lacks the ability to
generate specific centers. We manipulated the structure of the denoising U-Net
and tuned the input and output architecture parameters. The new architecture
can obtain image input information to guide the generative model with stronger
detail processing ability. For original Dreambooth, the input would be
placeholder token and images used for training. The trained model include
.ckpt file and related components such as a scheduler, text encoder,
tokenizer, U-Net and VAE. We replaced the conventional denoising U-Net in the
training files with our modified U-Net. Due to the different architectures,
the dimensions of the output parameters were also modified.

Figure 5: Examples of anchor images obtained from a Google search. The name of
each celebrity is located above the image. Figure 6: Comparison between SDM
[19] and TIDM generated outputs, using the corresponding text prompts noted at
top.

##  IV EXPERIMENTS

We extensively evaluate the effectiveness of our method through a series of
experiments. Our experiments specifically focus on generating forged images
that depict meetings between celebrities. The subjects in our forged images
primarily include influential leaders of different countries and celebrities
with international recognition, such as South African President Cyril
Ramaphosa and China’s wealthiest individual Jack Ma. On one hand, the presence
of prominent individuals in counterfeit visual content increases the
likelihood of spreading misinformation. Conversely, fake content that includes
well-known celebrities is relatively easy to evaluate subjectively, and
obtaining high-definition images of these individuals is also more accessible.
This reduced difficulty in counterfeiting makes such content a practical test-
bed for evaluating the effectiveness of our technique.

###  IV-A Datasets and Experimental Setup

Training images of the celebrities were obtained from Google searches. The
selection of such images aimed to emulate a realistic scenario of generating
counterfeit news through the manipulation of generative models. Specifically,
we trained our private custom model using a limited number of pictures for
each individual, ranging from $3$ to $5$, with the help of a modified
Dreambooth training file. The training process was executed on a computer
equipped with a 3090 graphics card, an i9-10900 CPU, and 128 GB of running
memory. The total training time was less than 20 minutes. In Fig. 5, we have
provided some examples of anchor images.

In our experiments, the images are resized to $512\times 512$ pixels. The
weights of the Huggingface model, which adopts the U-Net architecture and
comprises 860 million parameters, are stored in a .bin file. To ensure
fairness in comparisons, we adopt the Denoising Diffusion Implicit Model
(DDIM) [24] sampling method with 50 inference steps for image generation. The
number of inference steps is typically positively correlated with the quality
of generated images but also with the computational time. By employing the
DDIM, we achieve a balance between image quality and computational time. The
guidance scale for our model is 7.5, and to improve the utilization of
computational resources, our model generates four images simultaneously. The
inference process consists of two steps: (1) obtaining and saving the
Dreambooth model locally or in the cloud; (2) importing the specific model
into our custom pipeline to generate images. For successful image generation,
the prompt must match the placeholder token taught in the Dreambooth model.

###  IV-B Comparison with Stable Diffusion

Typical images generated by the SDM [19] and the corresponding images of the
proposed TIDM are displayed in Fig. 6. Each image pair in the figure uses the
same text prompt, mentioned at the top of each pair. In general, our
inspection of the images generated by SDM revealed that the realism of human
faces and the background is not satisfactory. One notable challenge
encountered by the SDM method is the blending of facial features when the
input prompt involves multiple subjects. As depicted in Fig. 7, we utilize the
text “a picture of Obama chatting with Jackie Chan” as an input prompt and
simultaneously generate 10 images. Among them, six images exhibit issues with
facial feature fusion, where the faces of the two individuals in the picture
appear quite similar. It is evident that the facial features primarily
resemble Jackie Chan’s face while also incorporating some of Obama’s facial
features. This phenomenon arises due to the limited ability of SDM to generate
multiple centers. Consequently, most SDM examples feature only a single
central object.

Figure 7:  Some examples of facial fusion features obtained from the SDM when
generating images based on the text prompt ”a picture of Obama chatting with
Jackie Chan”

To address this problem, our model trained with the Dreambooth method, is able
to use a specific subject identifier. This enables us to use latent
representation of a person’s image as a secondary input. Our solution
effectively resolves the blending problem. In Fig. 6, the second row showcases
the image output generated by TIDM. It is evident that our model has achieved
a significantly higher level of image generation quality by training with the
Dreambooth algorithm. our method clearly demonstrates a significant
improvement in the image quality as compared to the SDM output. It is
emphasized that the displayed images for the SDM are hand-picked from the
visually appealing outputs. Mostly, the SDM images suffered from obvious
problems, e.g., blending of faces, which are resolved by TIDM. In Fig. 8, we
exemplify the background control with our method where multiple images of
different celebrity meetings are generated with a background that can provide
consistency between the images. This background control is possible with the
proposed use of anchor image in the generation process.

Figure 8: Images generated with TIDM by controlling the background with anchor
images for consistency between images.

###  IV-C Subjective assessments

Subjective human evaluation of our model, compared to SDM model, was conducted
with ten academics from various fields. We provided prompts for ten unique
characters and generated ten images per prompt using both models. The
participants rated the realism of the images on a scale of 1 (“obvious fake”)
to 10 (“very convincing”). The results of the evaluation are presented in
Table I, which shows that our model significantly outperformed the stable
diffusion model in generating convincing images.

TABLE I: Subjective evaluation of images with average rating from 10 evaluators using a 1-10 score range for convincingness across 10 instances of each corresponding Image Id. Img. ID | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Avg.
---|---|---|---|---|---|---|---|---|---|---|---
SDM | 7.10 | 6.00 | 7.00 | 7.30 | 6.38 | 7.00 | 6.40 | 7.80 | 8.60 | 6.00 | 6.96
TIDM | 9.80 | 8.60 | 7.90 | 9.20 | 8.80 | 8.60 | 8.44 | 8.10 | 8.80 | 8.20 | 8.64

##  V Conclusion

This paper introduces an advanced method for generating highly realistic fake
images that can be utilized as convincing evidence of events, such as meetings
involving prominent public figures. Additionally, the proposed approach
enables the generation of multiple images with consistent backgrounds to
support rumors or misleading narratives. The primary objective of this
research is to emphasize the potential risks associated with AI-generated
visual content in the dissemination of fake news. By raising awareness about
these dangers, we aim to encourage proactive measures to mitigate the negative
impacts of misleading and manipulated visual media.

##  VI ACKNOWLEDGMENTS

This research was supported by National Intelligence and Security Discovery
Research Grants (project$\\#$ NS220100007), funded by the Department of
Defence Australia.

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