Adv-BDPM: Adversarial attack based on Boundary Diffusion Probability Model

Deep neural networks have become increasingly significant in our daily lives due to their remarkable performance. The issue of adversarial examples, which are responsible for the vulnerability problem of deep neural networks, has attracted the attention of researchers in the study of robustness of these networks. To address the issues caused by the restricted diversity and precision of adversarial perturbations in neural networks, we introduce a novel technique called Adversarial Boundary Diffusion Probability Modeling (Adv-BDPM). This approach combines boundary analysis and diffusion probability modeling. First, we combined the denoising diffusion probability model with the boundary loss to design the boundary diffusion probability model, which can generate corresponding boundary perturbations for a specific neural network. Then, through the iterative process of boundary perturbations and its corresponding orthogonal perturbations, we proposed a decision boundary search algorithm to generate adversarial samples. The comparison experiments with black-box attacks in ImageNet demonstrate that Adv-BDPM has better attack success rate and perturbation precision. The comparison experiments with white-box attacks in CIFAR-10 and CIFAR-100 demonstrate that AdvBDPM has better attack success rate, attack diversity for the same sample, and can effectively defend against adversarial training with shorter running time.