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arXiv · 2203.14315

Adaptive Frequency Learning in Two-branch Face Forgery Detection

Neng Wang·Yang Bai·Kun Yu·Yong Jiang·Shu-tao Xia·Yan Wang
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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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Neng Wang, Yang Bai, Kun Yu, Yong Jiang, Shu-tao Xia, Yan Wang. 2022-03-27. Adaptive Frequency Learning in Two-branch Face Forgery Detection. https://arxiv.org/abs/2203.14315

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