In recent years, face forgery detectors have aroused great interest and achieved impressive performance, but they are still struggling with generalization and robustness. In this work, we explore taking full advantage of the fine-grained forgery traces in both spatial and frequency domains to alleviate this issue. Specifically, we propose a novel High-Frequency Fine-Grained Transformer (F2Trans) network which contains two important components, namely Central Difference Attention (CDA) and High-frequency Wavelet Sampler (HWS). The premier CDA module is capable of capturing invariant fine-grained manipulation patterns by aggregating both pixel-level intensity and gradient information of the query to generate key and value pairs. Subsequently, the proposed HWS discards the low-frequency components of wavelet transformation and hierarchically explores high-frequency forgery cues of feature maps, which prevents model confusion caused by low-frequency components and pays attention to local frequency information. In addition, HWS can be employed as a special pooling layer for the F2Trans architecture to produce hierarchical feature representations in the spatial-frequency domain. Extensive experiments on multiple popular benchmarks demonstrate the generalization and robustness of the specially designed F2Trans framework is well-tailored for face forgery detection when confronting the cross-dataset, cross-manipulation, and unseen perturbations.

中文翻译：

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F2Trans：用于人脸伪造检测的高频细粒度变压器


近年来，人脸伪造检测器引起了人们的极大兴趣并取得了令人印象深刻的性能，但它们仍在通用性和鲁棒性方面遇到困难。在这项工作中，我们探索充分利用空间和频率域中的细粒度伪造痕迹来缓解这个问题。具体来说，我们提出了一种新颖的高频细粒度变压器（F2Trans）网络，其中包含两个重要组件，即中心差分注意（CDA）和高频小波采样器（HWS）。首要的 CDA 模块能够通过聚合查询的像素级强度和梯度信息来生成键和值对，从而捕获不变的细粒度操作模式。随后，所提出的HWS丢弃了小波变换的低频分量，并分层探索特征图的高频伪造线索，防止了低频分量引起的模型混乱，并关注局部频率信息。此外，HWS 可以用作 F2Trans 架构的特殊池化层，以在空间频率域中生成分层特征表示。对多个流行基准的大量实验表明，专门设计的 F2Trans 框架的泛化性和鲁棒性非常适合在面对跨数据集、交叉操纵和看不见的扰动时进行人脸伪造检测。