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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