  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.
