Generalizing Adversarial Examples by AdaBelief Optimizer

TL;DR

This paper introduces AB-FGSM, an adversarial attack method that integrates AdaBelief optimization to generate more transferable adversarial examples, improving attack success rates against various models.

Contribution

The paper proposes a novel AB-FGSM method combining AdaBelief optimizer with I-FGSM to enhance adversarial example transferability and effectiveness.

Findings

Transfer rate 7%-21% higher than state-of-the-art methods.

Effective in white-box and black-box attack scenarios.

Improves adversarial example generalization across models.

Abstract

Recent research has proved that deep neural networks (DNNs) are vulnerable to adversarial examples, the legitimate input added with imperceptible and well-designed perturbations can fool DNNs easily in the testing stage. However, most of the existing adversarial attacks are difficult to fool adversarially trained models. To solve this issue, we propose an AdaBelief iterative Fast Gradient Sign Method (AB-FGSM) to generalize adversarial examples. By integrating AdaBelief optimization algorithm to I-FGSM, we believe that the generalization of adversarial examples will be improved, relying on the strong generalization of AdaBelief optimizer. To validate the effectiveness and transferability of adversarial examples generated by our proposed AB-FGSM, we conduct the white-box and black-box attacks on various single models and ensemble models. Compared with state-of-the-art attack methods, our proposed method can generate adversarial examples effectively in the white-box setting, and the transfer rate is 7%-21% higher than latest attack methods.