通过多模型重建差异检测合成人脸，识别更准且泛化强。
Model Discrepancy Learning: Synthetic Faces Detection Based on Multi-Reconstruction
- 用多个生成模型反向重建图像，比对重建差异
- 在多个数据集上准确率超95%，跨模型泛化能力强
- 适合需要高鲁棒性检测的安防与内容审核场景
图像生成技术发展使合成人脸日益逼真，带来安全风险，因此合成人脸检测至关重要。以往研究关注生成图像与真实图像的一般差异，常忽视不同生成方法间的内在差异。本文探索合成图像与其生成技术之间的内在关系，发现特定图像在不同生成方法下存在显著重建差异，且匹配生成技术可实现更精准重建。基于此，提出基于多重建的检测器：通过多个生成模型对图像进行逆向重建，分析真实、GAN生成和扩散模型生成图像的重建差异，实现有效区分。同时构建亚洲合成人脸数据集（ASFD），包含多种GAN与扩散模型生成的亚洲人脸，补充现有数据集。实验表明，该检测器性能优异，具备强大泛化与鲁棒性。
原文摘要 · Abstract (English)
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.
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