Deepfakes and AI-edited faces are often too 'perfect', which can help deepfake detection systems to identify them. But a new academic collaboration has devised a way to inject real-world 'flaws' into fake or altered images so that they are much harder to distinguish from real photos.
This has two practical applications: one is that entirely synthetic images, such as those created with Generative Adversarial Networks (GANs) and other types of generative system (including Stable Diffusion) could potentially use this knowledge to create images that are even more realistic. The second is that AI-based image-editing systems (including deepfakes, which amend existing images and video rather than entirely replacing them) could be able to conceal evidence of changes…