Adversarial attacks are carried out to reveal the vulnerability of deep
neural networks. Textual adversarial attacking is challenging because text is
discrete and a small perturbation can bring significant change to the original
input. Word-level attacking, which can be regarded as a combinatorial
optimization problem, is a well-studied class of textual attack methods.
However, existing word-level attack models are far from perfect, largely
because unsuitable search space reduction methods and inefficient optimization
algorithms are employed. In this paper, we propose a novel attack model, which
incorporates the sememe-based word substitution method and particle swarm
optimization-based search algorithm to solve the two problems separately. We
conduct exhaustive experiments to evaluate our attack model by attacking BiLSTM
and BERT on three benchmark datasets. Experimental results demonstrate that our
model consistently achieves much higher attack success rates and crafts more
high-quality adversarial examples as compared to baseline methods. Also,
further experiments show our model has higher transferability and can bring
more robustness enhancement to victim models by adversarial training. All the
code and data of this paper can be obtained on
this https URL

本文提出了一种新颖的攻击模型，采用语义元替换和粒子群优化算法处理词级攻击中不适当的搜索空间缩减方法和低效的优化算法，实验结果表明该模型相对于基线方法在攻击成功率方面显著提高，可生成更高质量的对抗样本并通过对抗训练提高受害模型的鲁棒性。