PECAN: A Deterministic Certified Defense Against Backdoor Attacks
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni

TL;DR
PECAN is an efficient, certified defense method against backdoor attacks in neural networks, using test-time evasion certification on data partitions to significantly reduce attack success rates.
Contribution
PECAN introduces a novel, certified defense approach leveraging existing certification techniques on data partitions, outperforming prior methods in strength and efficiency.
Findings
Outperforms state-of-the-art certified defenses in strength and efficiency.
Reduces attack success rate by an order of magnitude on real backdoor attacks.
Effective on image classification and malware detection datasets.
Abstract
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…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Malware Detection Techniques · Anomaly Detection Techniques and Applications
MethodsTest
