STAIN-FL: Stealthy Targeted Attack Injection with Contextual Triggers in Federated Learning
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-dimensi...
| Authors | Ashlinder Kaur, Purnima Murali Mohan, Zengxiang Li et al. |
|---|---|
| Primary category | cs.CR |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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