Nchelem, Buduka Cherish and Singh, Amit Kumar and Mouratidis, Haralambos and Chatterjee, Urbi (2026) Detection of Dynamic Frame Alteration in IoT Video Streams. In: IEEE Conference on Artificial Intelligence, 2026-05-08 - 2026-05-10, Granada, Spain. (In Press)
Nchelem, Buduka Cherish and Singh, Amit Kumar and Mouratidis, Haralambos and Chatterjee, Urbi (2026) Detection of Dynamic Frame Alteration in IoT Video Streams. In: IEEE Conference on Artificial Intelligence, 2026-05-08 - 2026-05-10, Granada, Spain. (In Press)
Nchelem, Buduka Cherish and Singh, Amit Kumar and Mouratidis, Haralambos and Chatterjee, Urbi (2026) Detection of Dynamic Frame Alteration in IoT Video Streams. In: IEEE Conference on Artificial Intelligence, 2026-05-08 - 2026-05-10, Granada, Spain. (In Press)
Abstract
The rapid expansion of IoT video systems in security-critical environments has increased exposure to sophisticated video manipulation attacks. Traditional detection methods including machine learning approaches such as SVM, kNN, and LOF show strong performance against replay, frame injection, and stream hijacking attacks, but fail to detect Dynamic Frame Alteration (DFA), a subtle and adaptive manipulation technique that introduces minimal visual disturbance and leaves limited statistical traces in pixel space. To address this limitation, this work presents a real-time DFA detection framework that relies on system-level behavioral metrics rather than video content analysis. The proposed method monitors frame rate, CPU usage, memory load, and process activity using sliding time windows, rolling averages, and empirically validated thresholds, enabling robust detection. Experimental results show that DFA manipulation produces consistent deviations in these system metrics,particularly frame drops and CPU/memory spikes which are effectively captured by the proposed approach. Across two realistic scenarios the detector achieves up to 93% detection accuracy, significantly outperforming traditional machine learning models whose detection rates on DFA average below 25%.These findings establish system-metric analysis as a practical and explainable alternative to machine learning methods to detect advanced video manipulation in IoT environments.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Published proceedings: _not provided_ |
| Uncontrolled Keywords: | IoT Security, Video Manipulation, Dynamic Frame Alteration, Lightweight Detection, Edge Computing, Anomaly Detection, System Metrics, Embedded Systems |
| Subjects: | Z Bibliography. Library Science. Information Resources > ZR Rights Retention |
| Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 21 Apr 2026 11:35 |
| Last Modified: | 21 Apr 2026 11:37 |
| URI: | http://repository.essex.ac.uk/id/eprint/42823 |
Available files
Filename: Detection_of_Dynamic_Frame_Alteration_in_IoT_Video_Streams (1).pdf
Licence: Creative Commons: Attribution 4.0