Abstract
Advances in image generation enable hyper-realistic synthetic faces but also pose risks, thus making synthetic face detection crucial. Previous research focuses on the general differences between generated images and real images, often overlooking the discrepancies among various generative techniques. In this paper, we explore the intrinsic relationship between synthetic images and their corresponding generation technologies. We find that specific images exhibit significant reconstruction discrepancies across different generative methods and that matching generation techniques provide more accurate reconstructions. Based on this insight, we propose a Multi-Reconstruction-based detector. By reversing and reconstructing images using multiple generative models, we analyze the reconstruction differences among real, GAN-generated, and DM-generated images to facilitate effective differentiation. Additionally, we introduce the Asian Synthetic Face Dataset (ASFD), containing synthetic Asian faces generated with various GANs and DMs. This dataset complements existing synthetic face datasets. Experimental results demonstrate that our detector achieves exceptional performance, with strong generalization and robustness.
Abstract (translated)
图像生成技术的进步使得合成面部非常逼真，但也带来了风险，因此检测合成面部变得至关重要。以往的研究主要关注于生成图像和真实图像之间的总体差异，却往往忽略了不同生成技术之间存在的细微差别。在这篇论文中，我们探讨了合成图像与其相应生成技术之间的内在联系。我们发现特定的图像在不同的生成方法下会表现出显著的重建差异，并且使用匹配的生成技术能够提供更为准确的重建结果。基于这一洞察，我们提出了一个基于多重建的检测器（Multi-Reconstruction-based detector）。通过反向和重构来自多种生成模型的图像，我们可以分析真实图像、GAN生成图像以及DM生成图像之间的重建差异，从而实现有效的区分。 此外，为了支持我们的研究，我们引入了亚洲合成面部数据集（Asian Synthetic Face Dataset, ASFD），该数据集中包含使用各种GANs和DMs生成的不同类型的亚洲面孔。此数据集补充了现有的合成面部数据集，为研究提供了新的视角。 实验结果表明，我们的检测器在性能上表现出色，并且具有很强的泛化能力和鲁棒性。
URL
https://arxiv.org/abs/2504.07382
