HotFlip: White-Box Adversarial Examples for NLP
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Adversarial examples expose vulnerabilities of machine learning models. We propose an efficient method to generate white-box adversarial examples that trick character-level and word-level neural models. Our method, HotFlip, relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors. In experiments on text classification and machine translation, we find that only a few manipulations are needed to greatly increase the error rates. We analyze the properties of these examples, and show that employing these adversarial examples in training can improve test-time accuracy on clean examples, as well as defend the models against adversarial examples.
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