论文标题
用于面部伪造的多尺度小波变压器
Multi-Scale Wavelet Transformer for Face Forgery Detection
论文作者
论文摘要
当前，许多面对伪造检测方法汇总了空间和频率特征，以增强跨数据集情景下的概括能力并获得有希望的表现。但是，这些方法仅利用限制其表达能力的一个级别频率信息。为了克服这些局限性，我们提出了一个多尺度的小波变压器框架，以进行伪造检测。具体而言，为了充分利用多尺度和多频波表示，我们逐渐在骨干网络的不同阶段逐渐汇总了多尺度小波表示。为了更好地融合频率特征与空间特征，基于频率的空间注意力旨在指导空间特征提取器，以更多地集中在伪造轨迹上。同时，提出了交叉模式的关注，以将频率特征与空间特征融合在一起。这两个注意模块是通过统一的变压器块计算出的，以提高效率。各种各样的实验表明，所提出的方法对内部和交叉数据集都是有效且有效的。
Currently, many face forgery detection methods aggregate spatial and frequency features to enhance the generalization ability and gain promising performance under the cross-dataset scenario. However, these methods only leverage one level frequency information which limits their expressive ability. To overcome these limitations, we propose a multi-scale wavelet transformer framework for face forgery detection. Specifically, to take full advantage of the multi-scale and multi-frequency wavelet representation, we gradually aggregate the multi-scale wavelet representation at different stages of the backbone network. To better fuse the frequency feature with the spatial features, frequency-based spatial attention is designed to guide the spatial feature extractor to concentrate more on forgery traces. Meanwhile, cross-modality attention is proposed to fuse the frequency features with the spatial features. These two attention modules are calculated through a unified transformer block for efficiency. A wide variety of experiments demonstrate that the proposed method is efficient and effective for both within and cross datasets.
