Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866916341737324544 |
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| author | Di, Jimmy Z. Douglas, Jack Acharya, Jayadev Kamath, Gautam Sekhari, Ayush |
| author_facet | Di, Jimmy Z. Douglas, Jack Acharya, Jayadev Kamath, Gautam Sekhari, Ayush |
| contents | We introduce camouflaged data poisoning attacks, a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced. An adversary first adds a few carefully crafted points to the training dataset such that the impact on the model's predictions is minimal. The adversary subsequently triggers a request to remove a subset of the introduced points at which point the attack is unleashed and the model's predictions are negatively affected. In particular, we consider clean-label targeted attacks (in which the goal is to cause the model to misclassify a specific test point) on datasets including CIFAR-10, Imagenette, and Imagewoof. This attack is realized by constructing camouflage datapoints that mask the effect of a poisoned dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_10717 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks Di, Jimmy Z. Douglas, Jack Acharya, Jayadev Kamath, Gautam Sekhari, Ayush Machine Learning Artificial Intelligence Cryptography and Security Computers and Society We introduce camouflaged data poisoning attacks, a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced. An adversary first adds a few carefully crafted points to the training dataset such that the impact on the model's predictions is minimal. The adversary subsequently triggers a request to remove a subset of the introduced points at which point the attack is unleashed and the model's predictions are negatively affected. In particular, we consider clean-label targeted attacks (in which the goal is to cause the model to misclassify a specific test point) on datasets including CIFAR-10, Imagenette, and Imagewoof. This attack is realized by constructing camouflage datapoints that mask the effect of a poisoned dataset. |
| title | Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Computers and Society |
| url | https://arxiv.org/abs/2212.10717 |