  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.
