Textual adversarial attacks expose the vulnerabilities of text classifiers
and can be used to improve their robustness. Existing context-aware methods
solely consider the gold label probability and use the greedy search when
searching an attack path, often limiting the attack efficiency. To tackle these
issues, we propose PDBS, a context-aware textual adversarial attack model using
Probability Difference guided Beam Search. The probability difference is an
overall consideration of all class label probabilities, and PDBS uses it to
guide the selection of attack paths. In addition, PDBS uses the beam search to
find a successful attack path, thus avoiding suffering from limited search
space. Extensive experiments and human evaluation demonstrate that PDBS
outperforms previous best models in a series of evaluation metrics, especially
bringing up to a +19.5% attack success rate. Ablation studies and qualitative
analyses further confirm the efficiency of PDBS.

提出了一种名为 PDBS 的新的上下文感知的文本对抗攻击模型，它使用概率差异对攻击路径进行选择，并使用波束搜索来寻找成功的攻击路径，实验证明 PDBS 在一系列评估指标上优于以前的最佳模型，特别是攻击成功率提高了 19.5%。