We propose a general framework for increasing local stability of Artificial Neural Nets (ANNs) using Robust Optimization (RO). We achieve this through an alternating minimization-maximization procedure, in which the loss of the network is minimized over perturbed examples that are generated at each parameter update. We show that adversarial training of ANNs is in fact robustification of the network optimization, and that our proposed framework generalizes previous approaches for increasing local stability of ANNs. Experimental results reveal that our approach increases the robustness of the network to existing adversarial examples, while making it harder to generate new ones. Furthermore, our algorithm improves the accuracy of the network also on the original test data.

提出了一个使用鲁棒性优化（RO）增加人工神经网络（ANN）本地稳定性的通用框架。该算法是通过替代最小化-最大化过程实现的，该网络的损失是在每个参数更新时生成的扰动样本上最小化的。实验结果表明，该方法提高了网络对现有敌对示例的鲁棒性，同时使生成新的敌对示例更加困难，而且该算法还提高了网络在原始测试数据上的准确性。

理解对抗训练: 通过鲁棒优化提升神经网络局部稳定性