What is it about?
This article investigates the problem of face tampering in videos by presenting a deep learning–based forensic framework designed to detect deepfake manipulations. It analyzes visual artifacts and temporal inconsistencies introduced during face manipulation, evaluates the effectiveness of the proposed approach on video data, and demonstrates its potential for improving the reliability and trustworthiness of digital media analysis.
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Why is it important?
This article is important because it addresses the growing threat of manipulated video content by proposing deep learning–based forensic techniques that help detect face tampering and support the authenticity and trustworthiness of digital media.
Perspectives
This paper was motivated by growing concerns about the ease with which video content can be manipulated today. Through the exploration of deepfake forensics, technical research was aligned with a problem of clear societal relevance, and it is hoped that this work encourages continued efforts toward reliable and trustworthy media analysis.
Dr Omar S Al-Kadi
University of Jordan
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This page is a summary of: Detecting face tampering in videos using deepfake forensics, Multimedia Tools and Applications, April 2025, Springer Science + Business Media,
DOI: 10.1007/s11042-025-20865-4.
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