Explaining and Harnessing Adversarial Examples [PDF]

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. Moreover, this view yields a simple and fast method of generating adversarial examples.