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 .