Robustness Feature Adapter for Efficient Adversarial Training
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908500848803840 |
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| author | Wu, Quanwei Guo, Jun Wang, Wei Wang, Yi |
| author_facet | Wu, Quanwei Guo, Jun Wang, Wei Wang, Yi |
| contents | Adversarial training (AT) with projected gradient descent is the most popular method to improve model robustness under adversarial attacks. However, computational overheads become prohibitively large when AT is applied to large backbone models. AT is also known to have the issue of robust overfitting. This paper contributes to solving both problems simultaneously towards building more trustworthy foundation models. In particular, we propose a new adapter-based approach for efficient AT directly in the feature space. We show that the proposed adapter-based approach can improve the inner-loop convergence quality by eliminating robust overfitting. As a result, it significantly increases computational efficiency and improves model accuracy by generalizing adversarial robustness to unseen attacks. We demonstrate the effectiveness of the new adapter-based approach in different backbone architectures and in AT at scale. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_17680 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robustness Feature Adapter for Efficient Adversarial Training Wu, Quanwei Guo, Jun Wang, Wei Wang, Yi Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition I.2.6 Adversarial training (AT) with projected gradient descent is the most popular method to improve model robustness under adversarial attacks. However, computational overheads become prohibitively large when AT is applied to large backbone models. AT is also known to have the issue of robust overfitting. This paper contributes to solving both problems simultaneously towards building more trustworthy foundation models. In particular, we propose a new adapter-based approach for efficient AT directly in the feature space. We show that the proposed adapter-based approach can improve the inner-loop convergence quality by eliminating robust overfitting. As a result, it significantly increases computational efficiency and improves model accuracy by generalizing adversarial robustness to unseen attacks. We demonstrate the effectiveness of the new adapter-based approach in different backbone architectures and in AT at scale. |
| title | Robustness Feature Adapter for Efficient Adversarial Training |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition I.2.6 |
| url | https://arxiv.org/abs/2508.17680 |