Collected and annotated datasets, which are obtained through extensive
efforts, are effective for training Deep Neural Network (DNN) models. However,
these datasets are susceptible to be misused by unauthorized users, resulting
in infringement of Intellectual Property (IP) rights owned by the dataset
creators. Reversible Adversarial Exsamples (RAE) can help to solve the issues
of IP protection for datasets. RAEs are adversarial perturbed images that can
be restored to the original. As a cutting-edge approach, RAE scheme can serve
the purposes of preventing unauthorized users from engaging in malicious model
training, as well as ensuring the legitimate usage of authorized users.
Nevertheless, in the existing work, RAEs still rely on the embedded auxiliary
information for restoration, which may compromise their adversarial abilities.
In this paper, a novel self-generation and self-recovery method, named as
RAEDiff, is introduced for generating RAEs based on a Denoising Diffusion
Probabilistic Models (DDPM). It diffuses datasets into a Biased Gaussian
Distribution (BGD) and utilizes the prior knowledge of the DDPM for generating
and recovering RAEs. The experimental results demonstrate that RAEDiff
effectively self-generates adversarial perturbations for DNN models, including
Artificial Intelligence Generated Content (AIGC) models, while also exhibiting
significant self-recovery capabilities.

通过 DDPM 中的先验知识，将数据集扩散到偏差高斯分布 (BGD) 中，利用 RAEDiff 这种自我生成和自我恢复方法，生成并恢复用于深度神经网络 (DNN) 模型的可逆对抗扰动 (RAEs)，实验证明 RAEDiff 可有效自动生成 DNN 模型的对抗扰动，并具有显著的自我恢复能力。