论文标题
通过特征摩托车对抗攻击增强可传递性
Enhancing the Transferability via Feature-Momentum Adversarial Attack
论文作者
论文摘要
由于对现实世界应用的实际威胁，可转移的对抗攻击引起了人们的关注。特别是，特征级对抗性攻击是一个最近的分支，可以通过干扰中间特征来增强转移性。现有方法通常为特征创建指南图，其中值表示相应特征元素的重要性，然后采用迭代算法来相应地破坏功能。但是，指南图是在现有方法中固定的，当迭代过程中图像更改时，该方法无法始终如一地反映网络的行为。在本文中，我们描述了一种称为特征 - 摩诺特对抗攻击（FMAA）的新方法，以进一步提高可传递性。我们方法的关键思想是，我们使用动量在每次迭代时动态估算一个指导图，以有效地干扰与类别相关的特征。广泛的实验表明，我们的方法在不同的目标模型上大幅度的边距大大优于其他最先进的方法。
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.
