今天读了一下 Towards Evaluating the Robustness of Neural Networks，这是关于对抗样本(Adversarial Example)的paper，主要贡献是提出了Carlini & Wagner Attack神经网络有目标攻击算法，打破了最近提出的神经网络防御性蒸馏(Defensive Distillation)，证明防御性蒸馏不会显著提高神经网络的鲁棒性，论文的信息量还是比较大的。
可能需要阅读的论文以获取前置知识：
关于蒸馏网络：
[1] Do Deep Nets Really Need to be Deep?
[2] Distilling the Knowledge in a Neural Network
[3] Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
关于对抗样本：
[4] Intriguing properties of neural networks（L-BFGS Attack）
[5] Exploring the Space of Adversarial Images（FGSM）
[6] Towards Deep Learning Models Resistant to Adversarial Attacks（PGD, I-FGSM）
[7] The Limitations of Deep Learning in Adversarial Settings (JSMA)
[8] DeepFool: a simple and accurate method to fool deep neural networks（DeepFool）
关于论文源码：
