Gradient-based Adversarial Attacks Against Text Transformers
Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, Douwe Kiela . Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing 2021 – 55 citations
We propose the first general-purpose gradient-based attack against transformer models. Instead of searching for a single adversarial example, we search for a distribution of adversarial examples parameterized by a continuous-valued matrix, hence enabling gradient-based optimization. We empirically demonstrate that our white-box attack attains state-of-the-art attack performance on a variety of natural language tasks. Furthermore, we show that a powerful black-box transfer attack, enabled by sampling from the adversarial distribution, matches or exceeds existing methods, while only requiring hard-label outputs.