Adversarial Diffusion Model: Generating High-Quality and Undetectable Images From Scratch

Diffusion models have made tremendous progress in generating visually realistic images. However, these images are statistically different from the real images, which could be accurately classified by carefully designed detectors. To evade detection, researchers have proposed various adversarial example generation schemes for AI-generated images. Despite the progress, most of these schemes have the tendency to post-process the images and the distortion is inevitable. In this paper, we propose an Adversarial Diffusion Model (ADM), which is able to directly generate high quality and undetectable images from scratch on top of a pre-trained stable diffusion model. In the ADM, an adversarial denoising U-Net is proposed for searching an adversarial latent. This latent is helpful for generating a prompt consistent adversarial example which is able to deceive the detector. Then, we propose a latent compensation module to make the adversarial examples have a similar reconstruction error to that of the real images. We further propose an adversarial decoder to minimize the difference between the high-frequency components of the real and adversarial examples. Comprehensive experiments are carried out to demonstrate the advantages of our ADM in generating adversarial AI-generated images.