Adversarial Machine Learning at Scale.

Adversarial examples are malicious inputs designed fool machine learning models.They often transfer from one model another, allowing attackers mount blackbox attacks without knowledge of the target modelu0027s parameters.Adversarial training is the process of explicitly training a model on adversarialexamples, in order make it more robust or reduce its test erroron clean inputs.So far, training has primarily been applied small problems.In this research, we apply training ImageNet.Our contributions include:(1) recommendations for how succesfully scale training large models and datasets,(2) the observation that training confers robustness single-step methods,(3) the finding that multi-step methods are somewhat less transferable than single-step attack methods, so single-step attacks are the best for mounting black-box attacks, and(4) resolution of a ``label leakingu0027u0027 effect that causes adversarially trained models perform better on examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process.