Transferable Sparse Adversarial Attack

Code

• github.com/NiCE-X/TSAA
  Officialpytorch★ 2
• github.com/shaguopohuaizhe/TSAA
  In paperpytorch★ 35

Abstract

Deep neural networks have shown their vulnerability to adversarial attacks. In this paper, we focus on sparse adversarial attack based on the _0 norm constraint, which can succeed by only modifying a few pixels of an image. Despite a high attack success rate, prior sparse attack methods achieve a low transferability under the black-box protocol due to overfitting the target model. Therefore, we introduce a generator architecture to alleviate the overfitting issue and thus efficiently craft transferable sparse adversarial examples. Specifically, the generator decouples the sparse perturbation into amplitude and position components. We carefully design a random quantization operator to optimize these two components jointly in an end-to-end way. The experiment shows that our method has improved the transferability by a large margin under a similar sparsity setting compared with state-of-the-art methods. Moreover, our method achieves superior inference speed, 700 faster than other optimization-based methods. The code is available at https://github.com/shaguopohuaizhe/TSAA.

Tasks

Reproductions