Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks that would trigger misclassification of DNNs but may be imperceptible to human perception. Adversarial defense has been important ways to improve the robustness of DNNs. Existing attack methods often construct adversarial examples relying on some metrics like the $\ell_p$ distance to perturb samples. However, these metrics can be insufficient to conduct adversarial attacks due to their limited perturbations. In this paper, we propose a new internal Wasserstein distance (IWD) to capture the semantic similarity of two samples, and thus it helps to obtain larger perturbations than currently used metrics such as the $\ell_p$ distance We then apply the internal Wasserstein distance to perform adversarial attack and defense. In particular, we develop a novel attack method relying on IWD to calculate the similarities between an image and its adversarial examples. In this way, we can generate diverse and semantically similar adversarial examples that are more difficult to defend by existing defense methods. Moreover, we devise a new defense method relying on IWD to learn robust models against unseen adversarial examples. We provide both thorough theoretical and empirical evidence to support our methods.
翻译:众所周知,深心神经网络(DNNS)很容易受到敌对攻击,这种攻击会引发对DNS的错误分类,但可能无法为人所觉察。反向防御是提高DNS稳健性的重要方法。现有的攻击方法往往会建立依赖诸如美元/美元/美元距离至扰动样本等某些计量的对抗性例子。然而,这些指标可能不足以进行对抗性攻击,因为其干扰有限。在本文中,我们提出一个新的内部瓦塞尔斯坦距离(IWD),以捕捉两个样本的语义相似性,从而帮助获得比目前使用的数值(如$/ell_p$距离)更大的扰动性。我们随后运用内部瓦塞尔斯坦距离来进行对抗性攻击和防御。特别是,我们开发了一种新的攻击性方法,依靠IWD来计算图像与其对抗性例子之间的相似性。这样,我们可以产生多样化和语义性相似的对抗性对抗性对抗性例子,而现有的防御方法更难加以保护。此外,我们还设计一种新的防御方法,用以学习IWWD强的理论模型。