Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations
This work addresses a practical bottleneck in adversarial attacking for NLP, offering incremental improvements in efficiency for researchers and practitioners.
The paper tackles the inefficiency of textual adversarial attack models that require many queries to the victim model in real-world situations, proposing a reinforcement learning-based model that achieves better attack performance and higher efficiency than baseline methods across multiple tasks.
Adversarial attacking aims to fool deep neural networks with adversarial examples. In the field of natural language processing, various textual adversarial attack models have been proposed, varying in the accessibility to the victim model. Among them, the attack models that only require the output of the victim model are more fit for real-world situations of adversarial attacking. However, to achieve high attack performance, these models usually need to query the victim model too many times, which is neither efficient nor viable in practice. To tackle this problem, we propose a reinforcement learning based attack model, which can learn from attack history and launch attacks more efficiently. In experiments, we evaluate our model by attacking several state-of-the-art models on the benchmark datasets of multiple tasks including sentiment analysis, text classification and natural language inference. Experimental results demonstrate that our model consistently achieves both better attack performance and higher efficiency than recently proposed baseline methods. We also find our attack model can bring more robustness improvement to the victim model by adversarial training. All the code and data of this paper will be made public.