Enhancing the Transferability of Adversarial Examples with Random Patch

Adversarial examples can fool deep learning models, and their transferability is critical for attacking black-box models in real-world scenarios. Existing state-of-the-art transferable adversarial attacks tend to exploit intrinsic features of objects to generate adversarial examples. This paper proposes the Random Patch Attack (RPA) to significantly improve the transferability of adversarial examples by the patch-wise random transformation that effectively highlights important intrinsic features of objects. Specifically, we introduce random patch transformations to original images to variate model-specific features. Important object-related features are preserved after aggregating the transformed images since they stay consistent in multiple transformations while model-specific elements are neutralized. The obtained essential features steer noises to perturb the object-related regions, generating the adversarial examples of superior transferability across different models. Extensive experimental results demonstrate the effectiveness of the proposed RPA. Compared to the state-of-the-art transferable attacks, our attacks improve the black-box attack success rate by 2.9% against normally trained models, 4.7% against defense models, and 4.6% against vision transformers on average, reaching a maximum of 99.1%, 93.2%, and 87.8%, respectively.