Video deepfake detection using a hybrid CNN-LSTM-Transformer model for identity verification

The proliferation of deepfake technology poses significant challenges due to its potential for misuse in creating highly convincing manipulated videos. Deep learning (DL) techniques have emerged as powerful tools for analyzing and identifying subtle inconsistencies that distinguish genuine content f...

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Bibliographic Details
Published in:	Multimedia tools and applications Vol. 84; no. 33; pp. 40617 - 40636
Main Authors:	Petmezas, Georgios, Vanian, Vazgken, Konstantoudakis, Konstantinos, Almaloglou, Elena E. I., Zarpalas, Dimitris
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.10.2025 Springer Nature B.V
Subjects:	Artificial intelligence Artificial neural networks Biometrics Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Deception Deepfake Forensic computing Machine learning Multimedia Information Systems Neural networks Research methodology Spatial analysis Special Purpose and Application-Based Systems Support vector machines Track 6: Computer Vision for Multimedia Applications Verification Video compression Biometric authentication Transformer networks 3D Morphable Models (3DMMs) Identity verification Video deepfake detection Video forensics
ISSN:	1573-7721, 1380-7501, 1573-7721
Online Access:	Get full text
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