Provably effective detection of effective data poisoning attacks
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909462814523392 |
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| author | Gallagher, Jonathan Esfandiari, Yasaman MacPhee, Callen Warren, Michael |
| author_facet | Gallagher, Jonathan Esfandiari, Yasaman MacPhee, Callen Warren, Michael |
| contents | This paper establishes a mathematically precise definition of dataset poisoning attack and proves that the very act of effectively poisoning a dataset ensures that the attack can be effectively detected. On top of a mathematical guarantee that dataset poisoning is identifiable by a new statistical test that we call the Conformal Separability Test, we provide experimental evidence that we can adequately detect poisoning attempts in the real world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11795 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Provably effective detection of effective data poisoning attacks Gallagher, Jonathan Esfandiari, Yasaman MacPhee, Callen Warren, Michael Cryptography and Security Computer Vision and Pattern Recognition Machine Learning This paper establishes a mathematically precise definition of dataset poisoning attack and proves that the very act of effectively poisoning a dataset ensures that the attack can be effectively detected. On top of a mathematical guarantee that dataset poisoning is identifiable by a new statistical test that we call the Conformal Separability Test, we provide experimental evidence that we can adequately detect poisoning attempts in the real world. |
| title | Provably effective detection of effective data poisoning attacks |
| topic | Cryptography and Security Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2501.11795 |