Neural Machine Translation systems are used in diverse applications due to their impressive performance. However, recent studies have shown that these systems are vulnerable to carefully crafted small perturbations to their inputs, known as adversarial attacks. In this paper, we propose a new type of adversarial attack against NMT models. In this attack, we find a word to be added between two sentences such that the second sentence is ignored and not translated by the NMT model. The word added between the two sentences is such that the whole adversarial text is natural in the source language. This type of attack can be harmful in practical scenarios since the attacker can hide malicious information in the automatic translation made by the target NMT model. Our experiments show that different NMT models and translation tasks are vulnerable to this type of attack. Our attack can successfully force the NMT models to ignore the second part of the input in the translation for more than 50% of all cases while being able to maintain low perplexity for the whole input.

本研究针对神经机器翻译(NMT)模型的脆弱性，提出了一种新型的对抗攻击方法。研究者通过在两个句子之间插入一个词，使得第二个句子在翻译中被忽略，从而能够在实践中隐藏恶意信息。实验结果表明，超过50%的情况下，各种NMT模型对这种攻击都表现出脆弱性，且能维持较低的困惑度。

NMT-混淆攻击：用一个词忽略翻译中的一句话