The strength of Nesterov's accelerated gradient in boosting transferability of stealthy adversarial attacks.

Abstract

Deep neural networks have been shown to be highly vulnerable to adversarial examples-inputs crafted to mislead models by adding subtle, human-imperceptible perturbations. Transferability and stealthiness are two crucial metrics for evaluating adversarial attacks. However, these goals often conflict: examples with high transferability typically exhibit noticeable adversarial noise, while those with imperceptible perturbations tend to perform poorly in black-box attacks. To tackle this, we propose Diff-AdaNAG, a novel framework that introduces Nesterov's Accelerated Gradient (NAG) into diffusion-based adversarial example generation. Specifically, the diffusion mechanism guides the generation process toward the natural data distribution, achieving stealthy attacks with imperceptible adversarial examples. Meanwhile, an adaptive step-size strategy is utilized to harness the strong acceleration and generalization capabilities of NAG in optimization, enhancing black-box transferability in adversarial attacks. Extensive experiments demonstrate that Diff-AdaNAG consistently outperforms state-of-the-art methods in both white-box and black-box scenarios, significantly boosting transferability without compromising stealthiness. The code is available at https://github.com/Linc2021/Diff-AdaNAG.