Abstract
Face swapping, recognized as a privacy and security concern, has prompted considerable defensive research. With the advancements in AI-generated content, the discrepancies between the real and swapped faces have become nuanced. Considering the difficulty of forged traces detection, we shift the focus to the face swapping purpose and proactively embed elaborate watermarks against unknown face swapping techniques. Given that the constant purpose is to swap the original face identity while preserving the background, we concentrate on the regions surrounding the face to ensure robust watermark generation, while embedding the contour texture and face identity information to achieve progressive image determination. The watermark is located in the facial contour and contains hybrid messages, dubbed the contour-hybrid watermark (CMark). Our approach generalizes face swapping detection without requiring any swapping techniques during training and the storage of large-scale messages in advance. Experiments conducted across 8 face swapping techniques demonstrate the superiority of our approach compared with state-of-the-art passive and proactive detectors while achieving a favorable balance between the image quality and watermark robustness.
Abstract (translated)
面部替换，作为一种隐私和安全问题，已经引起了大量防御性研究的关注。随着人工智能生成内容的进步，真实人脸与被替换后的人脸之间的差异变得更加微妙。鉴于伪造痕迹检测的难度，我们将重点转移到面部替换的目的上，并主动嵌入复杂的水印以对抗未知的面部替换技术。考虑到面部替换的主要目的是在保持背景不变的情况下更换原始面部身份，我们专注于面部周围的区域来确保生成稳健的水印，同时嵌入口轮纹理和面部识别信息，实现渐进式图像判定。该水印位于面部轮廓中，并包含混合消息，被称为轮廓混合水印（CMark）。我们的方法可以泛化面部替换检测，在训练过程中不需要任何特定的替换技术，也不需要提前存储大规模的消息。 实验在八种不同的面部替换技术上进行，证明了我们方法相较于现有的被动和主动检测器具有优越性，同时实现了图像质量和水印稳健性的良好平衡。
URL
https://arxiv.org/abs/2505.19081
