Papers with white-box approach
Layerwise universal adversarial attack on NLP models (2023.findings-acl)
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| Challenge: | a new technique for layerwise UATs searches hidden layers of a network for universal adversarial triggers . a previous study showed that adversarials can fool models by perturbing samples that leave the ground truth label unchanged but can modify model prediction drastically. |
| Approach: | They propose a new approach to construct layerwise UATs by perturbing hidden layers of a network and propose LUATs that are more efficient than vanilla UAT methods. |
| Outcome: | The proposed method provides better transferability in a model-to-model setting with an average gain of 9.3% in fooling rate over baseline. |