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Dynamic-Aware Federated Learning for Face Forgery Video Detection

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Published:28 June 2022Publication History
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Abstract

The spread of face forgery videos is a serious threat to information credibility, calling for effective detection algorithms to identify them. Most existing methods have assumed a shared or centralized training set. However, in practice, data may be distributed on devices of different enterprises that cannot be centralized to share due to security and privacy restrictions. In this article, we propose a Federated Learning face forgery detection framework to train a global model collaboratively while keeping data on local devices. In order to make the detection model more robust, we propose a novel Inconsistency-Capture module (ICM) to capture the dynamic inconsistencies between adjacent frames of face forgery videos. The ICM contains two parallel branches. The first branch takes the whole face of adjacent frames as input to calculate a global inconsistency representation. The second branch focuses only on the inter-frame variation of critical regions to capture the local inconsistency. To the best of our knowledge, this is the first work to apply federated learning to face forgery video detection, which is trained with decentralized data. Extensive experiments show that the proposed framework achieves competitive performance compared with existing methods that are trained with centralized data, with higher-level security and privacy guarantee.

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        cover image ACM Transactions on Intelligent Systems and Technology
        ACM Transactions on Intelligent Systems and Technology  Volume 13, Issue 4
        August 2022
        364 pages
        ISSN:2157-6904
        EISSN:2157-6912
        DOI:10.1145/3522732
        • Editor:
        • Huan Liu
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        Publication History

        • Published: 28 June 2022
        • Online AM: 4 February 2022
        • Accepted: 1 November 2021
        • Revised: 1 July 2021
        • Received: 1 April 2021
        Published in tist Volume 13, Issue 4

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