  Deep neural networks are vulnerable to adversarial examples, which are
crafted by adding human-imperceptible perturbations to original images. Most
existing adversarial attack methods achieve nearly 100% attack success rates
under the white-box setting, but only achieve relatively low attack success
rates under the black-box setting. To improve the transferability of
adversarial examples for the black-box setting, several methods have been
proposed, e.g., input diversity, translation-invariant attack, and
momentum-based attack. In this paper, we propose a method named Gradient
Refining, which can further improve the adversarial transferability by
correcting useless gradients introduced by input diversity through multiple
transformations. Our method is generally applicable to many gradient-based
attack methods combined with input diversity. Extensive experiments are
conducted on the ImageNet dataset and our method can achieve an average
transfer success rate of 82.07% for three different models under single-model
setting, which outperforms the other state-of-the-art methods by a large margin
of 6.0% averagely. And we have applied the proposed method to the competition
CVPR 2021 Unrestricted Adversarial Attacks on ImageNet organized by Alibaba and
won the second place in attack success rates among 1558 teams.
