The Deepfake Litmus Test: A Multimedia Authenticity Mechanism
Document Type
Conference Proceeding
Source of Publication
Procedia Computer Science
Publication Date
1-1-2026
Abstract
Deepfake technologies have made it increasingly difficult to distinguish authentic video content from manipulated media. This paper presents a forensic detection framework, referred to as the Litmus Test, which focuses on structural analysis of MP4 container files to detect signs of tampering. Unlike conventional AI-based approaches that operate as black boxes, this method examines the atomic composition of video containers to identify anomalies. The proposed method performs atom-level inspection of MP4 file hierarchies and structural markers to uncover anomalies indicative of synthetic manipulation. Evaluations using datasets such as CelebDF, UADFV, and DeeperForensics reveal that the framework can identify inconsistencies common in deepfake media. The system offers explainable outputs suitable for forensic and legal applications, enabling verifiable detection grounded in digital forensics. By prioritizing interpretability, the framework supports forensic investigations by providing verifiable evidence that can meet the standards required for legal credibility.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
278
First Page
383
Last Page
391
Disciplines
Computer Sciences
Keywords
AI, deepfake, forensic analysis, MP4 structure, tamper detection
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Alzaabi, Amna; Alqubaisi, Hessa; Alzaabi, Fatima; and Ikuesan, Richard, "The Deepfake Litmus Test: A Multimedia Authenticity Mechanism" (2026). All Works. 8102.
https://zuscholars.zu.ac.ae/works/8102
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series