AI-Shielder: Exploiting Backdoors to Defend Against Adversarial Attacks

Summary

AI-Shielder is a defense that deliberately embeds backdoors in a DNN so that adversarial perturbations fail while the main task keeps working. Evaluated against sixteen adversarial example generation approaches, it reduces the attack success rate from 91.8% to 3.8%, outperforming state-of-the-art works by 37.2%, with a 0.6% decline in clean data accuracy and 1.43% overhead to model prediction time.

Topics

• Adversarial machine learning

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