Session 12D: ML Backdoors   Authors, Creators & Presenters: Hanlei Zhang (Zhejiang University), Yijie Bai (Zhejiang University), Yanjiao Chen (Zhejiang University), Zhongming Ma (Zhejiang University), Wenyuan Xu (Zhejiang University) PAPER BARBIE: Robust Backdoor Detection Based On Latent Separability Backdoor attacks are an essential risk to deep learning model sharing. Fundamentally, backdoored models are different from benign models considering latent separability, i.e., distinguishable differences in model latent representations. However, existing methods quantify latent separability by clustering latent representations or computing distances between latent representations, which are easy to be compromised by adaptive attacks. In this paper, we propose BARBIE, a backdoor detection approach that can pinpoint latent separability under adaptive backdoor attacks. To achieve this goal, we propose a new latent separability metric, named relative competition score (RCS), by characterizing the dominance of latent representations over model output, which is robust against various backdoor attacks and is hard to compromise. Without the need to access any benign or backdoored sample, we invert two sets of latent representations of each label, reflecting the normal latent representations of benign models and intensifying the abnormal ones of backdoored models, to calculate RCS. We compute a series of RCS-based indicators to comprehensively reflect the differences between backdoored models and benign models. We validate the effectiveness of BARBIE on more than 10,000 models on 4 datasets against 14 types of backdoor attacks, including the adaptive attacks against latent separability. Compared with 7 baselines, BARBIE improves the average true positive rate by 17.05% against source-agnostic attacks, 27.72% against source-specific attacks, 43.17% against sample-specific attacks and 11.48% against clean-label attacks. BARBIE also maintains lower false positive rates than baselines.  ABOUT NDSS The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.  Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.  Permalink

Security Boulevard is a leading cybersecurity news and information portal, offering articles, analysis, and insights on cybersecurity threats, vulnerabilities, and best practices. From the latest trends in cyber threats to expert commentary on security technologies and compliance frameworks, Security Boulevard provides resources for security professionals, IT leaders, and business executives seeking to protect their organizations from cyber attacks and data breaches.

Security Boulevard

BARBIE is a backdoor detection method for deep learning models presented at NDSS 2025. It introduces a novel latent separability metric called Relative Competition Score (RCS), which characterizes the dominance of latent representations over model output. Unlike existing approaches that rely on clustering or distance computation—making them vulnerable to adaptive attacks—BARBIE inverts two sets of latent representations per label without requiring access to benign or backdoored samples. Evaluated on over 10,000 models across 4 datasets against 14 backdoor attack types, BARBIE improves true positive rates by 17–43% over 7 baselines while maintaining lower false positive rates.

NDSS 2025 – NDSS 2025 – BARBIE: Robust Backdoor Detection Based On Latent Separability