用小波分析+Mamba架构，精准捕捉人脸伪造的细微痕迹。
WMamba: Wavelet-based Mamba for Face Forgery Detection
- 设计可变形卷积动态建模细长面部轮廓
- 基于Mamba实现线性复杂度长程特征捕捉
- 适合需要高精度伪造检测的安防与媒体审核场景
深度伪造技术的快速演进亟需鲁棒的人脸伪造检测算法。近期研究显示，小波分析能提升检测器的泛化能力。小波能有效捕捉关键面部轮廓——这些轮廓通常纤细、精细且全局分布，可能隐藏空间域中难以察觉的伪造痕迹。然而，现有小波方法未能充分利用小波数据的独特性质，导致特征提取不充分，性能提升有限。为此，本文提出WMamba，一种基于Mamba架构的小波特征提取新模型。通过两项创新：其一，提出动态轮廓卷积（DCConv），采用特殊设计的可变形核自适应建模细长面部轮廓；其二，利用Mamba架构以线性复杂度捕获长程空间关系。该效率使模型能够从小图像块中提取细粒度、全局分布的伪造痕迹。大量实验表明，WMamba达到当前最优（SOTA）性能，验证了其在人脸伪造检测中的有效性。
原文摘要 · Abstract (English)
The rapid evolution of deepfake generation technologies necessitates the development of robust face forgery detection algorithms. Recent studies have demonstrated that wavelet analysis can enhance the generalization abilities of forgery detectors. Wavelets effectively capture key facial contours, often slender, fine-grained, and globally distributed, that may conceal subtle forgery artifacts imperceptible in the spatial domain. However, current wavelet-based approaches fail to fully exploit the distinctive properties of wavelet data, resulting in sub-optimal feature extraction and limited performance gains. 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 complexity. This efficiency allows for the extraction of fine-grained, globally distributed forgery artifacts from small image patches. Extensive experiments show that WMamba achieves state-of-the-art (SOTA) performance, highlighting its effectiveness in face forgery detection.
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