A Relaxed Optimization Approach for Adversarial Attacks against Neural
  Machine Translation Models
Sahar Sadrizadeh Clément Barbier Ljiljana Dolamic Pascal Frossard
In this paper, we propose an optimization-based adversarial attack against
Neural Machine Translation (NMT) models. First, we propose an optimization
problem to generate adversarial examples that are semantically similar to the
original sentences but destroy the translation generated by the target NMT
model. This optimization problem is discrete, and we propose a continuous
relaxation to solve it. With this relaxation, we find a probability
distribution for each token in the adversarial example, and then we can
generate multiple adversarial examples by sampling from these distributions.
Experimental results show that our attack significantly degrades the
translation quality of multiple NMT models while maintaining the semantic
similarity between the original and adversarial sentences. Furthermore, our
attack outperforms the baselines in terms of success rate, similarity
preservation, effect on translation quality, and token error rate. Finally, we
propose a black-box extension of our attack by sampling from an optimized
probability distribution for a reference model whose gradients are accessible.