  Fast gradient sign attack series are popular methods that are used to
generate adversarial examples. However, most of the approaches based on fast
gradient sign attack series cannot balance the indistinguishability and
transferability due to the limitations of the basic sign structure. To address
this problem, we propose a method, called Adam Iterative Fast Gradient Tanh
Method (AI-FGTM), to generate indistinguishable adversarial examples with high
transferability. Besides, smaller kernels and dynamic step size are also
applied to generate adversarial examples for further increasing the attack
success rates. Extensive experiments on an ImageNet-compatible dataset show
that our method generates more indistinguishable adversarial examples and
achieves higher attack success rates without extra running time and resource.
Our best transfer-based attack NI-TI-DI-AITM can fool six classic defense
models with an average success rate of 89.3% and three advanced defense models
with an average success rate of 82.7%, which are higher than the
state-of-the-art gradient-based attacks. Additionally, our method can also
reduce nearly 20% mean perturbation. We expect that our method will serve as a
new baseline for generating adversarial examples with better transferability
and indistinguishability.
