Making Adversarial Examples More Transferable and Indistinguishable
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Many previous methods generate adversarial examples based on the fast gradient sign attack series. However, these methods cannot balance the indistinguishability and transferability due to the limitations of the basic sign structure. To address this problem, we propose an ADAM iterative fast gradient tanh method (AI-FGTM) to generate indistinguishable adversarial examples with high transferability. Extensive experiments on the ImageNet dataset show that our method generates more indistinguishable adversarial examples and achieves higher black-box attack success rates without extra running time and resource. Our best attack, TI-DI-AITM, can fool six black-box defense models with an average success rate of 88.0%. We expect that our method will serve as a new baseline for generating adversarial examples with more transferability and indistinguishability.
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