  Despite the success of input transformation-based attacks on boosting
adversarial transferability, the performance is unsatisfying due to the
ignorance of the discrepancy across models. In this paper, we propose a simple
but effective feature augmentation attack (FAUG) method, which improves
adversarial transferability without introducing extra computation costs.
Specifically, we inject the random noise into the intermediate features of the
model to enlarge the diversity of the attack gradient, thereby mitigating the
risk of overfitting to the specific model and notably amplifying adversarial
transferability. Moreover, our method can be combined with existing gradient
attacks to augment their performance further. Extensive experiments conducted
on the ImageNet dataset across CNN and transformer models corroborate the
efficacy of our method, e.g., we achieve improvement of +26.22% and +5.57% on
input transformation-based attacks and combination methods, respectively.
