解释与利用对抗样本
Explaining and Harnessing Adversarial Examples
Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy
AI 速览：作者提出神经网络对对抗扰动的脆弱性主要源于其线性本质，并据此给出快速生成对抗样本的方法及对抗训练效果。
Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset.