4.6k citations · 7.9k across the 144 of their papers we have counts for
7 papers · 1 filter
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation
Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard
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.…
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models
Sahar Sadrizadeh, Clément Barbier, Ljiljana Dolamic +1
In this paper, we propose an optimization-based adversarial attack against Neural Machine Translation (NMT) models. First, we propose an optimization problem to generate adversaria…
Targeted Adversarial Attacks against Neural Machine Translation
Sahar Sadrizadeh, AmirHossein Dabiri Aghdam, Ljiljana Dolamic +1
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…
TransFool: An Adversarial Attack against Neural Machine Translation Models
Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard
Deep neural networks have been shown to be vulnerable to small perturbations of their inputs, known as adversarial attacks. In this paper, we investigate the vulnerability of Neura…
Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers
Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard
Recently, it has been shown that, in spite of the significant performance of deep neural networks in different fields, those are vulnerable to adversarial examples. In this paper,…
QUACKIE: A NLP Classification Task With Ground Truth Explanations
Yves Rychener, Xavier Renard, Djamé Seddah +2
NLP Interpretability aims to increase trust in model predictions. This makes evaluating interpretability approaches a pressing issue. There are multiple datasets for evaluating NLP…