Adaptive Frequency Learning in Two-branch Face Forgery Detection

• URL: http://arxiv.org/abs/2203.14315v1
• Date: Sun, 27 Mar 2022 14:25:52 GMT
• Title: Adaptive Frequency Learning in Two-branch Face Forgery Detection
• Authors: Neng Wang, Yang Bai, Kun Yu, Yong Jiang, Shu-tao Xia, Yan Wang
• Abstract summary: We propose Adaptively learn Frequency information in the two-branch Detection framework, dubbed AFD. We liberate our network from the fixed frequency transforms, and achieve better performance with our data- and task-dependent transform layers.
• Score: 66.91715092251258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face forgery has attracted increasing attention in recent applications of computer vision. Existing detection techniques using the two-branch framework benefit a lot from a frequency perspective, yet are restricted by their fixed frequency decomposition and transform. In this paper, we propose to Adaptively learn Frequency information in the two-branch Detection framework, dubbed AFD. To be specific, we automatically learn decomposition in the frequency domain by introducing heterogeneity constraints, and propose an attention-based module to adaptively incorporate frequency features into spatial clues. Then we liberate our network from the fixed frequency transforms, and achieve better performance with our data- and task-dependent transform layers. Extensive experiments show that AFD generally outperforms.

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