A Codec-Based Approach for Video Life-Cycle Characterization in Social Networks

• Published in: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 4790 - 4794
• Main Authors: Bertazzini, Giulia, Baracchi, Daniele, Shullani, Dasara, Iuliani, Massimo, Piva, Alessandro
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.04.2024
• Subjects: Containers; DCT Coefficients; Feature extraction; Forensics; H.264/AVC Video Codec; Social Network Identification; Social networking (online); Speech coding; Streaming media; Video Forensics; Video sequences
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP48485.2024.10447289

Over the past decade, the proliferation of social networks introduced new challenges in the multimedia forensic field, such as the identification of the originating platform. Significant strides have been made in the characterization of digital images, exploiting features related to the media container and content. Within the realm of videos, several efforts have been directed towards analyzing the container aspect. However, the utilization of content-based features remains limited due to the intricate nature of video encoding. In this paper, we introduce an approach to identify the source social network of a digital video by leveraging codec-based features. For the purpose, we designed a method to extract and efficiently organize detailed information from H.264/AVC-encoded videos based on a bespoke version of the video decoder tool JM. We show how the proposed method can significantly improve the process of determining the source social network, even when confronted with container-based laundering operations, surpassing existing state-of-the-art results.