BERT-ATTACK: Adversarial Attack Against BERT Using BERT

Adversarial attacks for discrete data (such as text) has been proved significantly more challenging than continuous data (such as image), since it is difficult to generate adversarial samples with gradient-based methods. Currently, the successful attack methods for text usually adopt heuristic replacement strategies on character or word level, which remains challenging to find the optimal solution in the massive space of possible combination of replacements, while preserving semantic consistency and language fluency. In this paper, we propose \textbf{BERT-Attack}, a high-quality and effective method to generate adversarial samples using pre-trained masked language models exemplified by BERT. We turn BERT against its fine-tuned models and other deep neural models for downstream tasks. Our method successfully misleads the target models to predict incorrectly, outperforming state-of-the-art attack strategies in both success rate and perturb percentage, while the generated adversarial samples are fluent and semantically preserved. Also, the cost of calculation is low, thus possible for large-scale generations.