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.

通过改进扩散模型以生成可控高质量的深度伪造图像，并使用 Dreambooth 训练提供更真实和个性化的输出图像，该研究展示了以惊人的逼真度创建虚假的视觉内容，并可作为权力政治人物会面的可信证据。

文本图像指导的扩散模型用于生成深度伪造的名人互动

Text-image guided Diffusion Model for generating Deepfake celebrity  interactions

The rise of deepfake images, especially of well-known personalities, poses a
serious threat to the dissemination of authentic information. To tackle this,
we present a thorough investigation into how deepfakes are produced and how
they can be identified. The cornerstone of our research is a rich collection of
artificial celebrity faces, titled DeepFakeFace (DFF). We crafted the DFF
dataset using advanced diffusion models and have shared it with the community
through online platforms. This data serves as a robust foundation to train and
test algorithms designed to spot deepfakes. We carried out a thorough review of
the DFF dataset and suggest two evaluation methods to gauge the strength and
adaptability of deepfake recognition tools. The first method tests whether an
algorithm trained on one type of fake images can recognize those produced by
other methods. The second evaluates the algorithm's performance with imperfect
images, like those that are blurry, of low quality, or compressed. Given varied
results across deepfake methods and image changes, our findings stress the need
for better deepfake detectors. Our DFF dataset and tests aim to boost the
development of more effective tools against deepfakes.

通过对深度伪造图像的产生和检测进行深入研究，本论文提出了一种全面的深度伪造图像生成与识别的方法。在我们的研究中，我们创建了一个名为 DeepFakeFace (DFF) 的数据集，用于训练和测试检测深度伪造图像的算法。我们进行了对 DFF 数据集的全面评估，并提出了两种评估方法来测试深度伪造识别工具的强度和适应性。根据我们的发现，不同的深度伪造方法和图像变化产生了多种结果，突显了对深度伪造检测器的需求。我们的 DFF 数据集和测试旨在促进更有效的抵御深度伪造的工具的发展。