COMBAT: Alternated Training for Effective Clean-Label Backdoor Attacks
Tran Huynh,
Dang Nguyen,
Tung Pham
et al.
Abstract:Backdoor attacks pose a critical concern to the practice of using third-party data for AI development. The data can be poisoned to make a trained model misbehave when a predefined trigger pattern appears, granting the attackers illegal benefits. While most proposed backdoor attacks are dirty-label, clean-label attacks are more desirable by keeping data labels unchanged to dodge human inspection. However, designing a working clean-label attack is a challenging task, and existing clean-label attacks show underwh… Show more
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