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BagFlip: A Certified Defense Against Data Poisoning
Yuhao Zhang · Aws Albarghouthi · Loris D'Antoni

Thu Dec 01 09:00 AM -- 11:00 AM (PST) @ Hall J #1037

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.

Author Information

Yuhao Zhang (University of Wisconsin-Madison)
Aws Albarghouthi (University of Wisconsin, Madison)
Loris D'Antoni (University of Wisconsin, Madison)

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