论文标题
双分支伪造检测中的自适应频率学习
Adaptive Frequency Learning in Two-branch Face Forgery Detection
论文作者
论文摘要
在最近的计算机视觉应用中，伪造引起了人们越来越多的关注。使用频率的角度来看，使用两个分支框架的现有检测技术受益匪浅，但受其固定频率分解和变换的限制。在本文中，我们建议在称为AFD的两个分支检测框架中自适应地学习频率信息。要具体而言，我们通过引入异质性约束来自动学习频域中的分解，并提出一个基于注意力的模块，以将频率特征自适应地纳入空间线索。然后，我们将网络从固定频率变换中解放出来，并通过数据和任务依赖性转换层实现更好的性能。广泛的实验表明，AFD通常胜过。
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.
