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Open Access

IIN-FFD: Intra-Inter Network for Face Forgery Detection

School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
Institute of Artificial Intelligence, Guangzhou University, Guangzhou 510006, China
School of Computer Science, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
School of Computer Science, Hunan University, Changsha 410082, Hunan, China
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Abstract

Since different kinds of face forgeries leave similar forgery traces in videos, learning the common features from different kinds of forged faces would achieve promising generalization ability of forgery detection. Therefore, to accurately detect known forgeries while ensuring high generalization ability of detecting unknown forgeries, we propose an intra-inter network (IIN) for face forgery detection (FFD) in videos with continual learning. The proposed IIN mainly consists of three modules, i.e., intra-module, inter-module, and forged trace masking module (FTMM). Specifically, the intra-module is trained for each kind of face forgeries by supervised learning to extract special features, while the inter-module is trained by self-supervised learning to extract the common features. As a result, the common and special features of the different forgeries are decoupled by the two feature learning modules, and then the decoupled common features can be utlized to achieve high generalization ability for FFD. Moreover, the FTMM is deployed for contrastive learning to further improve detection accuracy. The experimental results on FaceForensic++ dataset demonstrate that the proposed IIN outperforms the state-of-the-arts in FFD. Also, the generalization ability of the IIN verified on DFDC and Celeb-DF datasets demonstrates that the proposed IIN significantly improves the generalization ability for FFD.

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Tsinghua Science and Technology
Pages 1839-1850
Cite this article:
Zhou Q, Zhou Z, Bao Z, et al. IIN-FFD: Intra-Inter Network for Face Forgery Detection. Tsinghua Science and Technology, 2024, 29(6): 1839-1850. https://doi.org/10.26599/TST.2024.9010022

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Received: 16 November 2023
Revised: 28 December 2023
Accepted: 21 January 2024
Published: 20 June 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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