Abstract
With the rapid advancement of deepfake generation technologies, the demand for robust and accurate face forgery detection algorithms has become increasingly critical. Recent studies have demonstrated that wavelet analysis can uncover subtle forgery artifacts that remain imperceptible in the spatial domain. Wavelets effectively capture important facial contours, which are often slender, fine-grained, and global in nature. However, existing wavelet-based approaches fail to fully leverage these unique characteristics, resulting in sub-optimal feature extraction and limited generalizability. To address this challenge, we introduce WMamba, a novel wavelet-based feature extractor built upon the Mamba architecture. WMamba maximizes the utility of wavelet information through two key innovations. First, we propose Dynamic Contour Convolution (DCConv), which employs specially crafted deformable kernels to adaptively model slender facial contours. Second, by leveraging the Mamba architecture, our method captures long-range spatial relationships with linear computational complexity. This efficiency allows for the extraction of fine-grained, global forgery artifacts from small image patches. Extensive experimental results show that WMamba achieves state-of-the-art (SOTA) performance, highlighting its effectiveness and superiority in face forgery detection.
Abstract (translated)
随着深度伪造生成技术的迅速发展，对稳健且准确的人脸伪造检测算法的需求变得愈发重要。近期研究显示，小波分析能够揭示在空间域中难以察觉的细微伪造痕迹。小波变换能有效地捕捉到重要的面部轮廓特征，这些特征往往是纤细、精细和具有全局性的。然而，现有的基于小波的方法未能充分利用这些独特特性，导致了次优的特征提取效果和有限的应用广度。 为了解决这一挑战，我们引入了一种新型的小波基特征提取器——WMamba，它是基于Mamba架构设计的。WMamba通过两个关键创新最大化了小波信息的作用。首先，我们提出了动态轮廓卷积（DCConv），采用特制的可变形核来适应性地建模纤细的面部轮廓；其次，利用Mamba架构的优势，我们的方法能够在线性计算复杂度下捕捉长距离的空间关系。这种效率使得可以从小型图像块中提取出精细、全局性的伪造痕迹。 广泛的实验结果表明，WMamba达到了最先进的性能（SOTA），突显了其在人脸伪造检测中的有效性和优越性。
URL
https://arxiv.org/abs/2501.09617
