Enhancing the Transferability via Feature-Momentum Adversarial Attack
This work addresses the threat of transferable adversarial attacks in real-world applications, representing an incremental improvement over prior feature-level methods.
The paper tackles the problem of improving transferability in adversarial attacks by dynamically updating the guidance map at each iteration using momentum, which significantly outperforms existing state-of-the-art methods on different target models.
Transferable adversarial attack has drawn increasing attention due to their practical threaten to real-world applications. In particular, the feature-level adversarial attack is one recent branch that can enhance the transferability via disturbing the intermediate features. The existing methods usually create a guidance map for features, where the value indicates the importance of the corresponding feature element and then employs an iterative algorithm to disrupt the features accordingly. However, the guidance map is fixed in existing methods, which can not consistently reflect the behavior of networks as the image is changed during iteration. In this paper, we describe a new method called Feature-Momentum Adversarial Attack (FMAA) to further improve transferability. The key idea of our method is that we estimate a guidance map dynamically at each iteration using momentum to effectively disturb the category-relevant features. Extensive experiments demonstrate that our method significantly outperforms other state-of-the-art methods by a large margin on different target models.