An Effective Way To Boost Black-Box Adversarial Attack

Deep neural networks (DNNs) are vulnerable to adversarial examples. Generally speaking adversarial examples are defined by adding input samples a small-magnitude perturbation, which is hardly misleading human observers' decision but would lead to misclassifications for a well trained models. Most of existing iterative adversarial attack methods suffer from low success rates in fooling model in a black-box manner. And we find that it is because perturbation neutralize each other in iterative process. To address this issue, we propose a novel boosted iterative method to effectively promote success rates. We conduct the experiments on ImageNet dataset, with five models normally trained for classification. The experimental results show that our proposed strategy can significantly improve success rates of fooling models in a black-box manner. Furthermore, it also outperforms the momentum iterative method (MI-FSGM), which won the first places in NeurIPS Non-targeted Adversarial Attack and Targeted Adversarial Attack competitions.