Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend
This work provides a novel interpretation of adversarial attacks in NLP, potentially aiding in developing more robust models, though it is incremental as it builds on existing attack understanding.
The study investigated word-level textual adversarial attacks by analyzing n-gram frequency patterns, finding that in about 90% of cases, attacks reduce n-gram frequency, termed n-gram Frequency Descend (n-FD). They proposed using n-FD to generate perturbed examples in adversarial training, achieving comparable robustness improvements to gradient-based methods.
Word-level textual adversarial attacks have demonstrated notable efficacy in misleading Natural Language Processing (NLP) models. Despite their success, the underlying reasons for their effectiveness and the fundamental characteristics of adversarial examples (AEs) remain obscure. This work aims to interpret word-level attacks by examining their $n$-gram frequency patterns. Our comprehensive experiments reveal that in approximately 90\% of cases, word-level attacks lead to the generation of examples where the frequency of $n$-grams decreases, a tendency we term as the $n$-gram Frequency Descend ($n$-FD). This finding suggests a straightforward strategy to enhance model robustness: training models using examples with $n$-FD. To examine the feasibility of this strategy, we employed the $n$-gram frequency information, as an alternative to conventional loss gradients, to generate perturbed examples in adversarial training. The experiment results indicate that the frequency-based approach performs comparably with the gradient-based approach in improving model robustness. Our research offers a novel and more intuitive perspective for understanding word-level textual adversarial attacks and proposes a new direction to improve model robustness.