Abstract
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.
Abstract (translated)
神经机器翻译系统因其出色的性能而被应用于各种不同的场合。然而，近期的研究表明，这些系统容易受到对其输入进行精心设计的小干扰的影响，这种干扰被称为对抗攻击。本文中，我们提出了一种新的针对NMT（神经机器翻译）模型的对抗攻击方法。在这种攻击中，我们在两个句子之间插入一个词，使得第二个句子被忽略并不会被NMT模型翻译。插入的这个单词能使整个对抗文本在源语言中显得自然。这种类型的攻击在实际场景中有害，因为攻击者可以利用目标NMT模型生成的自动翻译来隐藏恶意信息。我们的实验表明，不同的NMT模型和翻译任务都容易受到此类攻击的影响。该攻击能够成功地使NMT模型在超过50%的情况下忽略输入的第二部分进行翻译，并且在整个输入上保持较低的困惑度（perplexity）。
URL
https://arxiv.org/abs/2411.12473
