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FOCAL A Forgery Localization Framework based on Video Coding Self-Consistency

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Document pages: 13 pages

Abstract: Forgery operations on video contents are nowadays within the reach of anyone,thanks to the availability of powerful and user-friendly editing software.Integrity verification and authentication of videos represent a major interestin both journalism (e.g., fake news debunking) and legal environments dealingwith digital evidence (e.g., a court of law). While several strategies anddifferent forensics traces have been proposed in recent years, latest solutionsaim at increasing the accuracy by combining multiple detectors and features.This paper presents a video forgery localization framework that verifies theself-consistency of coding traces between and within video frames, by fusingthe information derived from a set of independent feature descriptors. Thefeature extraction step is carried out by means of an explainable convolutionalneural network architecture, specifically designed to look for and classifycoding artifacts. The overall framework was validated in two typical forgeryscenarios: temporal and spatial splicing. Experimental results show animprovement to the state-of-the-art on temporal splicing localization and alsopromising performance in the newly tackled case of spatial splicing, on bothsynthetic and real-world videos.

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