“Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data”, 2018-05-31 (; backlinks; similar):
We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks.
We demonstrate the effectiveness of these methods using both quantitative metrics and human evaluation on various state-of-the-art models for text classification, including a word-based CNN, a character-based CNN and an LSTM.
As as example of our results, we show that the accuracy of character-based convolutional networks drops to the level of random selection by modifying only 5 characters through Greedy Attack.