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The research of adversarial attacks in the text domain attracts manyinterests in the last few years, and many methods with a high attack successrate have been proposed. However, these attack methods are inefficient as theyrequire lots of queries for the victim model when crafting text adversarialexamples. In this paper, a novel attack model is proposed, its attack successrate surpasses the benchmark attack methods, but more importantly, its attackefficiency is much higher than the benchmark attack methods. The novel methodis empirically evaluated by attacking WordCNN, LSTM, BiLSTM, and BERT on fourbenchmark datasets. For instance, it achieves a 100\% attack success ratehigher than the state-of-the-art method when attacking BERT and BiLSTM on IMDB,but the number of queries for the victim models only is 1/4 and 1/6.5 of thestate-of-the-art method, respectively. Also, further experiments show the novelmethod has a good transferability on the generated adversarial examples.

Generating Natural Language Adversarial Examples through An Improved Beam Search Algorithm