Search Results for author:
Found 5 papers, 3 papers with code
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation
To evaluate the robustness of NMT models to our 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.
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation 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.
Targeted Adversarial Attacks against Neural Machine Translation
In this paper, we propose a new targeted adversarial attack against NMT models.
TransFool: An Adversarial Attack against Neural Machine Translation Models
Deep neural networks have been shown to be vulnerable to small perturbations of their inputs, known as adversarial attacks.
Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers
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.
