Model-Targeted Poisoning Attacks with Provable Convergence

• URL: http://arxiv.org/abs/2006.16469v2
• Date: Wed, 21 Apr 2021 13:40:37 GMT
• Title: Model-Targeted Poisoning Attacks with Provable Convergence
• Authors: Fnu Suya, Saeed Mahloujifar, Anshuman Suri, David Evans, Yuan Tian
• Abstract summary: In a poisoning attack, an adversary with control over a small fraction of the training data attempts to select that data in a way that induces a corrupted model. We consider poisoning attacks against convex machine learning models and propose an efficient poisoning attack designed to induce a specified model.
• Score: 19.196295769662186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In a poisoning attack, an adversary with control over a small fraction of the training data attempts to select that data in a way that induces a corrupted model that misbehaves in favor of the adversary. We consider poisoning attacks against convex machine learning models and propose an efficient poisoning attack designed to induce a specified model. Unlike previous model-targeted poisoning attacks, our attack comes with provable convergence to {\it any} attainable target classifier. The distance from the induced classifier to the target classifier is inversely proportional to the square root of the number of poisoning points. We also provide a lower bound on the minimum number of poisoning points needed to achieve a given target classifier. Our method uses online convex optimization, so finds poisoning points incrementally. This provides more flexibility than previous attacks which require a priori assumption about the number of poisoning points. Our attack is the first model-targeted poisoning attack that provides provable convergence for convex models, and in our experiments, it either exceeds or matches state-of-the-art attacks in terms of attack success rate and distance to the target model.

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