Generalizing Adversarial Examples by AdaBelief Optimizer
This addresses the vulnerability of deep neural networks to adversarial attacks, particularly for improving transferability in black-box settings, but it is incremental as it builds on existing optimization techniques.
The paper tackles the problem of adversarial examples failing against adversarially trained models by proposing AB-FGSM, which integrates AdaBelief optimization into I-FGSM, resulting in a 7%-21% higher transfer rate compared to state-of-the-art methods.
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.