# CEPA: Consensus Embedded Perturbation for Agnostic Detection and Inversion of Backdoors

Source: [CEPA: Consensus Embedded Perturbation for Agnostic Detection and Inversion of Backdoors](<https://www.sota2.com/research/paper/68fa77451e209131cea72213>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:14:52.638Z

Authors: Guangmingmei Yang, Xi Li, Hang Wang, David J. Miller, George Kesidis

Published: 2024-02-03T05:15:19.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2402.02034>)

[PDF](<https://arxiv.org/pdf/2402.02034.pdf>)