WMamba: 基于小波的Mamba人脸伪造检测方法
WMamba: Wavelet-based Mamba for Face Forgery Detection
AI总结：
针对现有小波人脸伪造检测方法未充分挖掘小波特性的问题，提出基于Mamba架构的WMamba，结合动态轮廓卷积与线性复杂度长程建模，实现人脸伪造检测的SOTA性能。
摘要：
深度伪造生成技术的快速迭代，亟需鲁棒的人脸伪造检测算法支撑。已有研究表明，小波分析可提升伪造检测器的泛化能力：小波能有效捕捉关键面部轮廓——这类轮廓通常纤细、细粒度且呈全局分布，可能隐藏空间域中无法察觉的细微伪造痕迹。但当前基于小波的方法未能充分挖掘小波数据的独特属性，导致特征提取效果欠佳、性能提升有限。为解决该问题，本文提出WMamba——一种基于Mamba架构的新型小波特征提取器。WMamba通过两项核心创新最大化小波信息利用率：其一，提出动态轮廓卷积（DCConv），采用特制可变形核自适应建模纤细面部轮廓；其二，借助Mamba架构以线性复杂度捕捉长程空间关系，该特性能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达到了当前最优（SOTA）性能，验证了其在人脸伪造检测中的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
