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arXiv · 1712.06751

HotFlip: White-Box Adversarial Examples for Text Classification

Javid Ebrahimi·Anyi Rao·Daniel Lowd·Dejing Dou
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Abstract

We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy. Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier as well.

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Javid Ebrahimi, Anyi Rao, Daniel Lowd, Dejing Dou. 2017-12-19. HotFlip: White-Box Adversarial Examples for Text Classification. https://arxiv.org/abs/1712.06751

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