Abstract
Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algorithms that have been attempted to thwart malicious Deepfake attacks, they mostly struggle with the generalizability challenge when confronted with hyper-realistic synthetic facial images. To tackle the problem, this paper proposes a proactive Deepfake detection approach by introducing a novel training-free landmark perceptual watermark, LampMark for short. We first analyze the structure-sensitive characteristics of Deepfake manipulations and devise a secure and confidential transformation pipeline from the structural representations, i.e. facial landmarks, to binary landmark perceptual watermarks. Subsequently, we present an end-to-end watermarking framework that imperceptibly and robustly embeds and extracts watermarks concerning the images to be protected. Relying on promising watermark recovery accuracies, Deepfake detection is accomplished by assessing the consistency between the content-matched landmark perceptual watermark and the robustly recovered watermark of the suspect image. Experimental results demonstrate the superior performance of our approach in watermark recovery and Deepfake detection compared to state-of-the-art methods across in-dataset, cross-dataset, and cross-manipulation scenarios.
Abstract (translated)
深度伪造面部操控因其对提升人类体验和构成隐私威胁的影响而获得了公众的广泛关注。尽管已经尝试了许多被动算法来阻止恶意的深度伪造攻击，但当面对超现实合成面部图像时，它们大多难以克服泛化挑战。为了解决这一问题，本文提出了一种基于主动防御的方法，并引入了一种无需训练的地标感知水印LampMark。我们首先分析了深度伪造操控的结构敏感特性，并设计了一个从结构表示（即面部地标）到二进制地标感知水印的安全且保密的转换管道。随后，我们提出了一个端到端的水印框架，该框架可以无损且鲁棒地嵌入和提取需要保护图像上的水印。依靠有前景的水印恢复准确性，通过评估内容匹配的地标感知水印与疑似图像中稳健恢复的水印之间的一致性来完成深度伪造检测。实验结果表明，在数据集内、跨数据集和跨操纵场景下，我们的方法在水印恢复和深度伪造检测方面均优于现有最先进的方法。
URL
https://arxiv.org/abs/2411.17209
