STAIN-FL: Stealthy Targeted Attack Injection with Contextual Triggers in Federated Learning

• URL: http://arxiv.org/abs/2608.23952v1
• Date: Tue, 25 Aug 2026 01:24:16 GMT
• Title: STAIN-FL: Stealthy Targeted Attack Injection with Contextual Triggers in Federated Learning
• Abstract summary: This paper introduces STAIN-FL, a stealthy targeted backdoor attack injection framework.<n>It uses naturally occurring surveillance conditions, including low-light scenes, indoor settings, and crowd density, as contextual triggers.<n>We evaluate STAIN-FL on textttUCF-Crime using 1024-dimensional I3D features in a non-IID four-client multi-agency setting.
• Score: 4.717908620608989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated video anomaly detection trains model collaboratively without sharing raw surveillance footage, but limited server-side visibility lets compromised clients to inject backdoor via malicious updates. This paper introduces STAIN-FL, a stealthy targeted backdoor attack injection framework that uses naturally occurring surveillance conditions, including low-light scenes, indoor settings, and crowd density, as contextual triggers. STAIN-FL combines anomaly-to-benign label \textit{manipulation} with gradient masking over least-updated coordinates to preserve clean accuracy while inducing trigger-conditioned misclassification. We evaluate STAIN-FL on \texttt{UCF-Crime} using 1024-dimensional I3D features in a non-IID four-client multi-agency setting, comparing FedAvg and FedProx under sparse and continuous attacks. Results show that sparse attacks have low-detectability, operationally significant attacks rather than high-intensity attacks: they keep the mean clean-accuracy drop below $2\%$, yet still misclassify more than half of triggered anomalies at peak backdoor accuracy under FedAvg ($56.7\%$) and FedProx ($54.2\%$). Under FedAvg, the sparse backdoor remains above the $25\%$ backdoor-accuracy threshold for an average of $336$ post-attack rounds, highlighting the persistence risk of contextually triggered attacks in surveillance systems.

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