The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application. Among many developed defense models against such attacks, adversarial training emerges as the most successful method that consistently resists a wide range of attacks. In this work, based on an observation from a previous study that the representations of a clean data example and its adversarial examples become more divergent in higher layers of a deep neural net, we propose the Adversary Divergence Reduction Network which enforces local/global compactness and the clustering assumption over an intermediate layer of a deep neural network. We conduct comprehensive experiments to understand the isolating behavior of each component (i.e., local/global compactness and the clustering assumption) and compare our proposed model with state-of-the-art adversarial training methods. The experimental results demonstrate that augmenting adversarial training with our proposed components can further improve the robustness of the network, leading to higher unperturbed and adversarial predictive performances.

本文提出了对抗性分歧减小网络（Adversary Divergence Reduction Network），旨在通过对深度神经网络中间层的本地/全局紧凑性和聚类假设的强制执行，增强对抗训练的鲁棒性，并与现有的最新对抗性训练方法进行了比较，实验证明该方法可以进一步提高网络的鲁棒性和预测性能。

通过实行局部和全局紧凑性以提高对抗鲁棒性