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White-to-black: Efficient Distillation Of Black-box Adversarial Attacks

Yotam Gil, Yoav Chai, Or Gorodissky, Jonathan Berant . Proceedings of the 2019 Conference of the North 2019 – 41 citations

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Compositional Generalization Content Enrichment Efficiency Image Text Integration Interactive Environments Interdisciplinary Approaches Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Question Answering Security Tools Training Techniques

Adversarial examples are important for understanding the behavior of neural models, and can improve their robustness through adversarial training. Recent work in natural language processing generated adversarial examples by assuming white-box access to the attacked model, and optimizing the input directly against it (Ebrahimi et al., 2018). In this work, we show that the knowledge implicit in the optimization procedure can be distilled into another more efficient neural network. We train a model to emulate the behavior of a white-box attack and show that it generalizes well across examples. Moreover, it reduces adversarial example generation time by 19x-39x. We also show that our approach transfers to a black-box setting, by attacking The Google Perspective API and exposing its vulnerability. Our attack flips the API-predicted label in 42% of the generated examples, while humans maintain high-accuracy in predicting the gold label.

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