Machine learning has a backdoor problem


Machine learning has a backdoor problem

Machine learning has a backdoor problem

“Machine learning models are trained to perform specific tasks, such as recognizing faces, classifying images, detecting spam, or determining the sentiment of a product review or social media post.

Machine learning backdoors are techniques that implant secret behaviors into trained ML models. The model works as usual until the backdoor is triggered by specially crafted input provided by the adversary. For example, an adversary can create a backdoor that bypasses a face recognition system used to authenticate users.

A simple and well-known ML backdooring method is data poisoning. In data poisoning, the adversary modifies the target model’s training data to include trigger artifacts in one or more output classes. The model then becomes sensitive to the backdoor pattern and triggers the intended behavior (e.g., the target output class) whenever it sees it…”


May 29, 2022
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