FastAttacker: Semantic Perturbation Functions via Three Classifications

Abstract

Deep neural networks (DNNs) have achieved great success in tasks such as image classification, speech recognition, and natural language processing. However, they are susceptible to false predictions caused by adversarial exemplars, which are normal inputs with imperceptible perturbations. Adversarial samples have been widely studied in image classification, but not as much in text classification. Current textual attack methods often rely on low-success-rate heuristic replacement strategies at the character or word level, which cannot search for the best solution while maintaining semantic consistency and linguistic fluency. Our framework, FastAttacker, generates natural adversarial text efficiently and effectively by constructing different semantic perturbation functions. It optimizes perturbations constrained in generic semantic spaces, such as the typo space, knowledge space, contextualized semantic space, or a c ombination. As a result, the generated adversarial texts are semantica lly close to the original inputs. Experiments show that FastAttacker generates adversarial texts from different levels of spatial constraints, making the problem of fi nding synonyms an optimal solution problem. Our approach is not onl y robust in terms of attack generation, but also in terms of adversarial defense. Experiments have shown that state-of-the-art language models and defense strategies are still vulnerable to FastAttack attacks.