All in One：以鲁棒关键点-身份水印统一深度伪造检测、篡改定位与来源溯源
All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity Watermark
把 152 维人脸关键点与来源标识交织成结构化水印，并用回归/分类双头解码器从共享骨干特征中稳健恢复，为视觉取证提供了一种可同时完成检测、定位与溯源的主动式嵌入-提取范式。
原文依据 a novel Factorized-Head Decoder (FHD)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论随着深度伪造技术的快速发展，恶意人脸操纵对个人隐私与社会安全构成重大威胁。然而，现有的主动取证方法通常将深度伪造检测、篡改定位与来源溯源视为彼此独立的任务，缺乏一个能够联合处理它们的统一框架。为弥补这一空白，我们提出一个统一的主动取证框架，联合解决上述三项核心任务。我们的核心框架采用一种创新的 152 维关键点-身份水印，称为 LIDMark，它在结构上将人脸关键点与唯一的来源标识交织在一起。为稳健地提取 LIDMark，我们设计了一种新颖的因子化检测头解码器（Factorized-Head Decoder, FHD）。其架构将共享骨干网络特征因子化为两个专用检测头（即回归与分类），分别稳健地重建所嵌入的关键点与标识符，即使在遭受严重失真或篡改时亦然。该设计实现了一种“一体化”的三功能取证方案：回归检测头支撑用于检测与定位的“内在-外在”一致性校验，而分类检测头则稳健地解码来源标识符以完成溯源。大量实验表明，所提出的 LIDMark 框架为深度伪造内容的检测、定位与溯源提供了一种统一、稳健且不可察觉的解决方案。代码已发布于 https://github.com/vpsg-research/LIDMark。
英文原文摘要 1490 字符
With the rapid advancement of deepfake technology, malicious face manipulations pose a significant threat to personal privacy and social security. However, existing proactive forensics methods typically treat deepfake detection, tampering localization, and source tracing as independent tasks, lacking a unified framework to address them jointly. To bridge this gap, we propose a unified proactive forensics framework that jointly addresses these three core tasks. Our core framework adopts an innovative 152-dimensional landmark-identity watermark termed LIDMark, which structurally interweaves facial landmarks with a unique source identifier. To robustly extract the LIDMark, we design a novel Factorized-Head Decoder (FHD). Its architecture factorizes the shared backbone features into two specialized heads (i.e., regression and classification), robustly reconstructing the embedded landmarks and identifier, respectively, even when subjected to severe distortion or tampering. This design realizes an "all-in-one" trifunctional forensic solution: the regression head underlies an "intrinsic-extrinsic" consistency check for detection and localization, while the classification head robustly decodes the source identifier for tracing. Extensive experiments show that the proposed LIDMark framework provides a unified, robust, and imperceptible solution for the detection, localization, and tracing of deepfake content. The code is available at https://github.com/vpsg-research/LIDMark.