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A gated temporal attention based intra prediction framework for robust deepfake video detection.

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Bibliographische Detailangaben
Titel:	A gated temporal attention based intra prediction framework for robust deepfake video detection.
Autoren:	Clapten, J. Eric¹ (AUTHOR) ericclaptenj@gmail.com, Balaji, v.² (AUTHOR)
Quelle:	Scientific Reports. 11/4/2025, Vol. 15 Issue 1, p1-28. 28p.
Schlagwörter:	VIDEO processing, INFORMATION technology security, CONVOLUTIONAL neural networks, AUTOENCODERS
Abstract:	The increasing use of manipulated videos has raised serious concerns in digital security and media authenticity. Deepfakes are particularly challenging to detect due to their realistic facial movements and seamless frame transitions. To overcome the challenges, a deepfake detection model named IP-GTA Net is proposed by combining intra prediction with temporal attention modeling. The approach begins by dividing each video frame into fixed-size blocks and reconstructing them using a hybrid convolutional autoencoder. This process simulates compression effects and highlights hidden visual inconsistencies. The reconstructed frames are then passed through MobileNetV3 to extract spatial features. Further gated convolutional GRU with temporal attention is used to model frame-wise transitions and detect manipulation across sequences. The proposed model was tested using the Celeb-DF dataset and achieved an accuracy of 90.52% and F1-score of 0.9051 which is better than existing XceptionNet, Two-Stream CNN, and EfficientNet-B0 models. [ABSTRACT FROM AUTHOR]
Datenbank:	Academic Search Index

Beschreibung
Abstract:	The increasing use of manipulated videos has raised serious concerns in digital security and media authenticity. Deepfakes are particularly challenging to detect due to their realistic facial movements and seamless frame transitions. To overcome the challenges, a deepfake detection model named IP-GTA Net is proposed by combining intra prediction with temporal attention modeling. The approach begins by dividing each video frame into fixed-size blocks and reconstructing them using a hybrid convolutional autoencoder. This process simulates compression effects and highlights hidden visual inconsistencies. The reconstructed frames are then passed through MobileNetV3 to extract spatial features. Further gated convolutional GRU with temporal attention is used to model frame-wise transitions and detect manipulation across sequences. The proposed model was tested using the Celeb-DF dataset and achieved an accuracy of 90.52% and F1-score of 0.9051 which is better than existing XceptionNet, Two-Stream CNN, and EfficientNet-B0 models. [ABSTRACT FROM AUTHOR]
ISSN:	20452322
DOI:	10.1038/s41598-025-22333-3