BagFlip: A Certified Defense against Data Poisoning

• URL: http://arxiv.org/abs/2205.13634v1
• Date: Thu, 26 May 2022 21:09:24 GMT
• Title: BagFlip: A Certified Defense against Data Poisoning
• Authors: Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
• Abstract summary: BagFlip is a model-agnostic certified approach that can effectively defend against both trigger-less and backdoor attacks. We evaluate BagFlip on image classification and malware detection datasets.
• Score: 15.44806926189642
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning models are vulnerable to data-poisoning attacks, in which an attacker maliciously modifies the training set to change the prediction of a learned model. In a trigger-less attack, the attacker can modify the training set but not the test inputs, while in a backdoor attack the attacker can also modify test inputs. Existing model-agnostic defense approaches either cannot handle backdoor attacks or do not provide effective certificates (i.e., a proof of a defense). We present BagFlip, a model-agnostic certified approach that can effectively defend against both trigger-less and backdoor attacks. We evaluate BagFlip on image classification and malware detection datasets. BagFlip is equal to or more effective than the state-of-the-art approaches for trigger-less attacks and more effective than the state-of-the-art approaches for backdoor attacks.

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