TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911009129627648 |
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| author | Mahato, Dipesh Tharu Poudel, Rohan Dhungana, Pramod |
| author_facet | Mahato, Dipesh Tharu Poudel, Rohan Dhungana, Pramod |
| contents | Deep neural networks often achieve high accuracy, but ensuring their reliability under adversarial and distributional shifts remains a pressing challenge. We propose TriGuard, a unified safety evaluation framework that combines (1) formal robustness verification, (2) attribution entropy to quantify saliency concentration, and (3) a novel Attribution Drift Score measuring explanation stability. TriGuard reveals critical mismatches between model accuracy and interpretability: verified models can still exhibit unstable reasoning, and attribution-based signals provide complementary safety insights beyond adversarial accuracy. Extensive experiments across three datasets and five architectures show how TriGuard uncovers subtle fragilities in neural reasoning. We further demonstrate that entropy-regularized training reduces explanation drift without sacrificing performance. TriGuard advances the frontier in robust, interpretable model evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14217 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift Mahato, Dipesh Tharu Poudel, Rohan Dhungana, Pramod Machine Learning Artificial Intelligence Deep neural networks often achieve high accuracy, but ensuring their reliability under adversarial and distributional shifts remains a pressing challenge. We propose TriGuard, a unified safety evaluation framework that combines (1) formal robustness verification, (2) attribution entropy to quantify saliency concentration, and (3) a novel Attribution Drift Score measuring explanation stability. TriGuard reveals critical mismatches between model accuracy and interpretability: verified models can still exhibit unstable reasoning, and attribution-based signals provide complementary safety insights beyond adversarial accuracy. Extensive experiments across three datasets and five architectures show how TriGuard uncovers subtle fragilities in neural reasoning. We further demonstrate that entropy-regularized training reduces explanation drift without sacrificing performance. TriGuard advances the frontier in robust, interpretable model evaluation. |
| title | TriGuard: Testing Model Safety with Attribution Entropy, Verification, and Drift |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.14217 |