TA: A Chinese Adversarial Samples Generation Approach Based on Multi-Strategy Perturbations

While adversarial attacks on English texts have been extensively investigated, research on Chinese language scenarios remains comparatively under-developed. To address this gap, we present TextAttack (TA), a novel Chinese adversarial sample generation approach that leverages the linguistic and pictographic characteristics of Chinese through multi-strategy perturbations. we propose a three-phase methodology to deal with the challenge systematically: The Key Token Extractor (KTE) employs confidence variation to perform segmented identification of critical sentences and pivotal tokens; The Weighted Multi-strategy Perturbation (WMP) module implements targeted weighted perturbations through a variety of strategies such as glyph-based substitution and synonym replacement; The Phonetic-Visual-Semantic (PVS) constrained iterative search mechanism optimizes perturbation selection while maintaining textual naturalness through multimodal constraint integration. Experiments show that TA performs well on six classification models across two datasets, reducing the classification accuracy of hotel review dataset and news topic dataset by over 44.1% and 72.2%, respectively, with a small cost and high transfer ability.