{"ID":2921725,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-03T05:56:00.181519634Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01225","arxiv_id":"2606.01225","title":"Privacy-Preserving Smart Surveillance with Cross-Dataset Violence Detection and Decentralized Evidence Governance","abstract":"AI-enabled surveillance can accelerate public-safety response, yet most systems still leave recorded evidence under centralized administrative control. This paper proposes a privacy-preserving smart surveillance framework that separates incident detection from evidence disclosure. A lightweight MobileNetV2-based video classifier detects violent clips, while each recorded incident segment is immediately encrypted and made accessible only through threshold-based approval. The decryption key is split with Shamir's Secret Sharing, member shares are protected with public-key cryptography, and voting is supported by time-limited tokens, two-factor authentication, signatures, and audit logs. This study evaluates MobileNetV2+LSTM, MobileNetV2+BiLSTM, and MobileNetV2+temporal CNN heads on SCVD, RWF-2000, and Real-Life Violence Situations under seven in-domain and cross-dataset scenarios. The best all-source model, MobileNetV2+BiLSTM, reaches 93.5% test accuracy and ROC-AUC 0.980% on the merged held-out set, while lower RWF-2000 slice performance confirms persistent dataset shift.","short_abstract":"AI-enabled surveillance can accelerate public-safety response, yet most systems still leave recorded evidence under centralized administrative control. This paper proposes a privacy-preserving smart surveillance framework that separates incident detection from evidence disclosure. A lightweight MobileNetV2-based video...","url_abs":"https://arxiv.org/abs/2606.01225","url_pdf":"https://arxiv.org/pdf/2606.01225v1","authors":"[\"Hasan Coşkun\",\"Furkan Çolhak\",\"Andrea Kulakov\",\"Vesna Dimitrova\"]","published":"2026-05-31T13:16:41Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}