MOBTAG: Multi-Objective Optimization Based Textual Adversarial Example Generation

Natural language processing (NLP) models are vulnerable to adversarial examples. Generating high-quality adversarial examples, which expose the vulnerability of NLP models and can be used to evaluate and improve their robustness, deserves further research. Existing techniques of generating adversarial examples in the NLP field are typically based on greedy synonym replacements, which may result in out-of-context and unnatural perturbations, and are easily identifiable by humans. In this paper, we present MOB-TAG, a Multi-objective Optimization based Textual Adversarial Example Generation method, which includes three types of perturbations, and utilizes pre-trained models such as BERT and RoBERTa to generate high-quality adversarial examples. MOBTAG generates fluent and grammatical output through a mask-then-infill procedure, with introducing multi-objective optimization and genetic algorithm to pursue a high attack success rate while maintaining a high level of similarity and readability. Experimental results show that compared with methods such as TextFooler, BERTAttack, and CLARE, MOBTAG improves the attack success rate and the textual similarity by at least 11.8% and 0.09 on average, respectively.