PECAN: A Deterministic Certified Defense Against Backdoor Attacks
This addresses a critical security vulnerability in neural networks for applications like image classification and malware detection, offering a deterministic certified defense where prior methods lacked formal guarantees or were inefficient.
The paper tackles the problem of backdoor poisoning attacks on neural networks by introducing PECAN, an efficient and certified defense that significantly outperforms state-of-the-art methods, reducing attack success rates by an order of magnitude on real attacks.
Neural networks are vulnerable to backdoor poisoning attacks, where the attackers maliciously poison the training set and insert triggers into the test input to change the prediction of the victim model. Existing defenses for backdoor attacks either provide no formal guarantees or come with expensive-to-compute and ineffective probabilistic guarantees. We present PECAN, an efficient and certified approach for defending against backdoor attacks. The key insight powering PECAN is to apply off-the-shelf test-time evasion certification techniques on a set of neural networks trained on disjoint partitions of the data. We evaluate PECAN on image classification and malware detection datasets. Our results demonstrate that PECAN can (1) significantly outperform the state-of-the-art certified backdoor defense, both in defense strength and efficiency, and (2) on real back-door attacks, PECAN can reduce attack success rate by order of magnitude when compared to a range of baselines from the literature.