  Neural Machine Translation (NMT) systems are used in various applications.
However, it has been shown that they are vulnerable to very small perturbations
of their inputs, known as adversarial attacks. In this paper, we propose a new
targeted adversarial attack against NMT models. In particular, our goal is to
insert a predefined target keyword into the translation of the adversarial
sentence while maintaining similarity between the original sentence and the
perturbed one in the source domain. To this aim, we propose an optimization
problem, including an adversarial loss term and a similarity term. We use
gradient projection in the embedding space to craft an adversarial sentence.
Experimental results show that our attack outperforms Seq2Sick, the other
targeted adversarial attack against NMT models, in terms of success rate and
decrease in translation quality. Our attack succeeds in inserting a keyword
into the translation for more than 75% of sentences while similarity with the
original sentence stays preserved.
