A Prototype Web Application to Support Human-Centered Audiovisual Content Authentication and Crowdsourcing

Nikolaos Vryzas, Anastasia Katsaounidou, Lazaros Vrysis, Rigas Kotsakis and Charalampos Dimoulas
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Nikolaos Vryzas: Multidisciplinary Media & Mediated Communication Research Group (M3C), Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Anastasia Katsaounidou: Multidisciplinary Media & Mediated Communication Research Group (M3C), Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Lazaros Vrysis: Multidisciplinary Media & Mediated Communication Research Group (M3C), Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Rigas Kotsakis: Multidisciplinary Media & Mediated Communication Research Group (M3C), Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Charalampos Dimoulas: Multidisciplinary Media & Mediated Communication Research Group (M3C), Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Future Internet, 2022, vol. 14, issue 3, 1-17

Abstract: Media authentication relies on the detection of inconsistencies that may indicate malicious editing in audio and video files. Traditionally, authentication processes are performed by forensics professionals using dedicated tools. There is rich research on the automation of this procedure, but the results do not yet guarantee the feasibility of providing automated tools. In the current approach, a computer-supported toolbox is presented, providing online functionality for assisting technically inexperienced users (journalists or the public) to investigate visually the consistency of audio streams. Several algorithms based on previous research have been incorporated on the backend of the proposed system, including a novel CNN model that performs a Signal-to-Reverberation-Ratio (SRR) estimation with a mean square error of 2.9%. The user can access the web application online through a web browser. After providing an audio/video file or a YouTube link, the application returns as output a set of interactive visualizations that can allow the user to investigate the authenticity of the file. The visualizations are generated based on the outcomes of Digital Signal Processing and Machine Learning models. The files are stored in a database, along with their analysis results and annotation. Following a crowdsourcing methodology, users are allowed to contribute by annotating files from the dataset concerning their authenticity. The evaluation version of the web application is publicly available online.

Keywords: tampering; authentication; misinformation; web application; news; machine learning; deep learning; crowdsourcing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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