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Collected and annotated datasets, which are obtained through extensiveefforts, are effective for training Deep Neural Network (DNN) models. However,these datasets are susceptible to be misused by unauthorized users, resultingin infringement of Intellectual Property (IP) rights owned by the datasetcreators. Reversible Adversarial Exsamples (RAE) can help to solve the issuesof IP protection for datasets. RAEs are adversarial perturbed images that canbe restored to the original. As a cutting-edge approach, RAE scheme can servethe purposes of preventing unauthorized users from engaging in malicious modeltraining, as well as ensuring the legitimate usage of authorized users.Nevertheless, in the existing work, RAEs still rely on the embedded auxiliaryinformation for restoration, which may compromise their adversarial abilities.In this paper, a novel self-generation and self-recovery method, named asRAEDiff, is introduced for generating RAEs based on a Denoising DiffusionProbabilistic Models (DDPM). It diffuses datasets into a Biased GaussianDistribution (BGD) and utilizes the prior knowledge of the DDPM for generatingand recovering RAEs. The experimental results demonstrate that RAEDiffeffectively self-generates adversarial perturbations for DNN models, includingArtificial Intelligence Generated Content (AIGC) models, while also exhibitingsignificant self-recovery capabilities.

RAEDiff: Denoising Diffusion Probabilistic Models Based Reversible Adversarial Examples Self-Generation and Self-Recovery