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一个基于分数的概率比率框架,用于在法医科学中深度假冒图像识别

Tianli Guo¹, Jisong Li¹, Yunqi Tang²

¹School of Criminal Investigation, People's Public Security University of China, Beijing, 100038, China.

Scientific reports

|March 4, 2026

概括

这项研究引入了一种基于分数的概率比率系统,用于深度假冒图像的法医识别. 该系统对法医应用具有前景,在基准数据集上实现了高准确度.

科学领域:

数字法医学数字法医学
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

深度假冒技术给数字媒体识别带来了重大挑战.
现有的方法很难跟上深度假冒生成的快速进步.

研究的目的:

开发和评估一个强大的基于分数的概率比率系统,用于法医识别深度假冒图像.
解决目前在法医背景下深度假冒检测技术的局限性.

主要方法:

使用FaceForensics++数据集与视频级分区来防止数据泄露.
评估了六个候选模型,确定囊探测器是最强大的.
采用内核密度估计用于分数分布建模和优化带宽.
应用经验边界和池相邻违规者 (PAV) 校准,以优化概率比.

主要成果:

囊探测器在FF++测试组上实现了0.983的曲线下的面积 (AUC).
该系统显示了低误导性证据率 (RMEP=0.069,RMED=0.092) 和良好的错误控制 (EER=0.0804).
在未见的数据集上进行的概括测试显示了可变的性能 (AUC 0.621-0.783),其中UADFV.的性能最高.

结论:

拟议的概率比率系统显示了法医深度假冒识别的潜力.
在实际法医应用之前,需要在各种现实场景中进行进一步的验证.

关键词:

深度假冒图像深度假冒图像检测检测检测检测检测标识识别识别识别在这里,我们可以看到LRLRLRLR.获得得分的得分.

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