眼睛不撒谎:用眼睛的线索揭露假面孔
Kuldeep Umesh Kawar1, Silpa Nair1, Navjot Kaur Kanwal2
1Department of Criminology and Forensic Science, Dr. Harisingh Gour Vishwavidyalaya, Sagar, Madhya Pradesh 470003, India.
Forensic science international
|March 14, 2026
概括
眼睛几何特征在受控条件下有效检测合成面部,特别是基于GAN的面部. 扩散生成面部的检测因数据集而异,突出显示了强大的深度假冒检测的上下文依赖性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 生物识别信息 生物识别信息
背景情况:
- 像GAN和扩散模型这样的生成模型迅速发展,创造出现实的合成面孔.
- 区分合成和真实面孔对于安全和真实性验证至关重要.
- 现有的检测方法需要适应生成图像的视觉保真度日益提高.
研究的目的:
- 评估眼睛几何特征,用于从GAN (StyleGAN2/3) 和扩散模型 (SFHQ-T2I) 中检测合成的面部.
- 在受控 (CFD) 和不受控制 (FFHQ,CelebA-HQ) 采购条件下评估检测性能.
- 为了确定最有效的功能,并了解跨生成方法和数据集的性能变化.
主要方法:
- 使用MediaPipe Face Mesh提取的面部地标.
- 用于二进制分类的XGBoost分类器 (真实与合成).
- 在不同数据集的双向和聚合设置中进行的分析.
主要成果:
- 在受控条件下强烈检测到基于GAN的工件 (CFD与StyleGAN2,AUC99.11%).
- 与不受控制的数据集相比,有效检测到扩散生成的面部 (FFHQ,CelebA-HQ,AUC 84.36-86.47%).
- 垂直误调被确定为一个关键的歧视性特征 (AUC 65.85-97.56%),尽管由于数据集异质性 (AUC 65.66-67.36%) 的原因,整体性能下降了.
结论:
- 面部检测的生理线索取决于上下文.
- 检测框架必须适应不断变化的生成模型能力.
- 建议的眼部特征方法对受控图像是可靠的,作为分选工具和多式联络系统组件.
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