CoFFEE: a codec-based forensic feature extraction and evaluation software for H.264 videos

• Published in: EURASIP Journal on Information Security Vol. 2024; no. 1; pp. 34 - 12
• Main Authors: Bertazzini, Giulia, Baracchi, Daniele, Shullani, Dasara, Iuliani, Massimo, Piva, Alessandro
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 23.10.2024; Springer Nature B.V; SpringerOpen
• Subjects: Codec; Coding; Communications Engineering; Engineering; Feature extraction; Forensic computing; H.264/AVC video codec; Image compression; Image manipulation; Multimedia forensics; Networks; Security Science and Technology; Signal,Image and Speech Processing; Social networks; Software; Systems and Data Security; Task complexity; Video compression; Video forensics; Video sequences; Feature extraction; Video sequences; H.264/AVC video codec; Video forensics; Multimedia forensics
• ISSN: 2510-523X; 1687-4161; 2510-523X; 1687-417X
• DOI: 10.1186/s13635-024-00181-4

The forensic analysis of digital videos is becoming increasingly relevant to deal with forensic cases, propaganda, and fake news. The research community has developed numerous forensic tools to address various challenges, such as integrity verification, manipulation detection, and source characterization. Each tool exploits characteristic traces to reconstruct the video life-cycle. Among these traces, a significant source of information is provided by the specific way in which the video has been encoded. While several tools are available to analyze codec-related information for images, a similar approach has been overlooked for videos, since video codecs are extremely complex and involve the analysis of a huge amount of data. In this paper, we present a new tool designed for extracting and parsing a plethora of video compression information from H.264 encoded files, including macroblocks structure, prediction residuals, and motion vectors. We demonstrate how the extracted features can be effectively exploited to address various forensic tasks, such as social network identification, source characterization, and double compression detection. We provide a detailed description of the developed software, which is released free of charge to enable its use by the research community to create new tools for forensic analysis of video files.