Robustness Verification for Contrastive Learning
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22865-22883, 2022.
Contrastive adversarial training has successfully improved the robustness of contrastive learning (CL). However, the robustness metric used in these methods is linked to attack algorithms, image labels and downstream tasks, all of which may affect the consistency and reliability of robustness metric for CL. To address these problems, this paper proposes a novel Robustness Verification framework for Contrastive Learning (RVCL). Furthermore, we use extreme value theory to reveal the relationship between the robust radius of the CL encoder and that of the supervised downstream task. Extensive experimental results on various benchmark models and datasets verify our theoretical findings, and further demonstrate that our proposed RVCL is able to evaluate the robustness of both models and images. Our code is available at https://github.com/wzekai99/RVCL.