Papers by Pascal Frossard
DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications (2023.acl-long)
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| Challenge: | Several explainability methods have been shown to be brittle in the face of adversarial perturbations of their inputs in the image and generic textual domains. |
| Approach: | They propose to adapt existing attribution robustness estimation methods to take into account domain-specific plausibility and to train networks that display robust attributions. |
| Outcome: | The proposed methods are able to characterize domain-specific plausibility and provide robust explanations on biomedical datasets. |
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation (2024.eacl-long)
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| Challenge: | Extensive research has been devoted to adversarial attacks against NMT models . perturbations of inputs can mislead the target model, resulting in incorrect outputs . |
| Approach: | They propose an adversarial attack framework that alters the class of output translations of an NMT model and a classifier to craft adversarials whose translations belong to a different class . |
| Outcome: | The proposed approach has a more substantial effect on the translation by altering the overall meaning, which leads to a different class determined by an oracle classifier. |