Explainable copy-move forgery detection in videos: Generative adversarial network-based transfer learning and multi-aspect transformer with shuffle attention.

Authors

M Raghavendra Reddy, C Anbu Ananth, B Santhosh Kumar

Published in

Journal of forensic sciences. Aug 16, 2026. Epub Aug 16, 2026.

Abstract

With the nascent reliance on unmanned aerial systems as a means of surveillance, security, and monitoring, it is essential to ensure that video data legitimacy is assured. Object-based Copy-Move Forgery (CMF), in which an object is either copied or moved in the frame or across a frame to alter the visual description, is one of the major threats to video integrity. Such manipulations are a problem for traditional detection methods because object motion, occlusion, and irregular background are complex. This research presents an interpretable and robust deep learning architecture to identify such forgeries, that is, a combination of transfer learning with a new Multi-Aspect Fossa Graph Transformer enhanced with Shuffle Attention (MAFGTN-SA). To achieve this, a better generative adversarial network, which is built upon GoogLeNet, is used to extract deep spatial-semantic features using normalized grayscale video frames. These characteristics are then represented by MAFGTN-SA to represent complex object relationships between the frames and spatial inconsistencies to accurately classify genuine and tampered frames. In order to increase interpretability, the model incorporates Local Interpretable Model-Agnostic Explanations (LIME) for saliency-based visualization of tampered regions. Experimental evaluations on three benchmark datasets, SULFA, GRIP, VTD, CG-1050 v2.0, and COVERAGE, validate the effectiveness of the proposed method, attaining an accuracy of 98.73%, 96.78%, 98.45%, 97.92%, and 97.36%, respectively, and with high F1-scores of 98.73%, 98.5%, 97.98%, 97.41%, and 96.93%. The results highlight the framework's superior detection capability, reliability, and explainability across diverse video forgery scenarios.

PMID:
42604591
Bibliographic data and abstract were imported from PubMed on 17 Aug 2026.

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