CNN Ensemble for Video Source Camera Forensics Article

Veksler, M, Aygun, R, Akkaya, K et al. (2024). CNN Ensemble for Video Source Camera Forensics . IEEE MULTIMEDIA, 10.1109/MMUL.2024.3372372

cited authors

  • Veksler, M; Aygun, R; Akkaya, K; Iyengar, S



  • The advancement of IoT technologies influenced a substantial increase in the usage of multimedia devices. Consequently, forensic specialists began to experience a vast load of video and image data during the investigative procedures. This triggered a need to ensure the integrity of multimedia data and verify its source origin for digital forensics processes. In this paper, we address the problem of identifying the video source camera of the video data acquired by the investigators. We develop a novel convolutional neural networks (CNNs) ensemble framework to identify the video source camera. In our method, we analyze the video data using patches extracted from I-frames quadrants (i.e., non-overlapping squares) using independent CNNs for each quadrant to achieve location awareness. Experimental results demonstrate that our framework is robust for the same device type classification and outperforms existing deep learning-based techniques.

publication date

  • January 1, 2024

published in

Digital Object Identifier (DOI)