CEPA: Consensus Embedded Perturbation for Agnostic Detection and Inversion of Backdoors
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Abstract (arXiv)
A variety of defenses have been proposed against Trojans planted in (backdoor attacks on) deep neural network (DNN) classifiers. Backdoor-agnostic methods seek to reliably detect and/or to mitigate backdoors irrespective of the incorporation mechanism used by the attacker, while inversion methods explicitly assume one. In this paper, we describe a new detector that: relies on embedded feature representations to estimate (invert) the backdoor and to identify its target class; can operate without access to the training dataset; and is highly effective for various incorporation mechanisms (i.e., is backdoor agnostic). Our detection approach is evaluated -- and found to be favorable - in comparison with an array of published defenses for a variety of different attacks on the CIFAR-10 and CIFAR-100 image-classification domains.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 26 references that are in the map. First posted on arXiv 3 Feb 2024.
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19 of 26 references.
References · 19
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7 more
- 2021Adversarial Unlearning of Backdoors via Implicit Hypergradientcited by 160
- 2019TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systemscited by 120
- 2020Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Casescited by 93
- 2022MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statisticcited by 53
- 2023UNICORN: A Unified Backdoor Trigger Inversion Frameworkcited by 44
- 2022Rethinking the Reverse-engineering of Trojan Triggerscited by 37
- 2021Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoringcited by 16
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