Neural Machine Translation (NMT) models have been shown to be vulnerable to
adversarial attacks, wherein carefully crafted perturbations of the input can
mislead the target model. In this paper, we introduce ACT, a novel adversarial
attack framework against NMT systems guided by a classifier. In our attack, the
adversary aims to craft meaning-preserving adversarial examples whose
translations by the NMT model belong to a different class than the original
translations in the target language. Unlike previous attacks, our new approach
has a more substantial effect on the translation by altering the overall
meaning, which leads to a different class determined by a classifier. To
evaluate the robustness of NMT models to this attack, we propose enhancements
to existing black-box word-replacement-based attacks by incorporating output
translations of the target NMT model and the output logits of a classifier
within the attack process. Extensive experiments in various settings, including
a comparison with existing untargeted attacks, demonstrate that the proposed
attack is considerably more successful in altering the class of the output
translation and has more effect on the translation. This new paradigm can show
the vulnerabilities of NMT systems by focusing on the class of translation
rather than the mere translation quality as studied traditionally.

我们提出了 ACT，一种新的针对 NMT 系统的对抗性攻击框架，通过一个分类器对其进行指导，以改变翻译的类别而非仅仅翻译质量，该攻击具有更大的影响力。