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

Text-image guided Diffusion Model for generating Deepfake celebrity interactions