Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples

24 Jan 2019Kamil NarOrhan OcalS. Shankar SastryKannan Ramchandran

State-of-the-art neural networks are vulnerable to adversarial examples; they can easily misclassify inputs that are imperceptibly different than their training and test data. In this work, we establish that the use of cross-entropy loss function and the low-rank features of the training data have responsibility for the existence of these inputs... (read more)

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