GM-Attack: Improving the Transferability of Adversarial Attacks.

In the real world, blackbox attacks seem to be widely existed due to the lack of detailed information of models to be attacked. Hence, it is desirable to obtain adversarial examples with high transferability which will facilitate practical adversarial attacks. Instead of adopting traditional input transformation approaches, we propose a mechanism to derive masked images through removing some regions from the initial input images. In this manuscript, the removed regions are spatially uniformly distributed squares. For comparison, several transferable attack methods are adopted as the baselines. Eventually, extensive empirical evaluations are conducted on the standard ImageNet dataset to validate the effectiveness of GM-Attack. As indicated, our GM-Attack can craft more transferable adversarial examples compared with other input transformation methods and attack success rate on Inc-v4 has been improved by 6.5% over state-of-the-art methods.