使用基于DCNN的面部相似度得分的DeepFake检测法医评估方法
Paulo Max Gil Innocencio Reis1, Rafael Oliveira Ribeiro2
1National Institute of Criminalistics, SPO Lote 7, Setores Complementares, ZIP Code:70610-902 Brasilia, DF, Brazil.
Forensic science international
|June 16, 2023
概括
本研究介绍了一种深度卷积神经网络 (DCNN) 方法,用于检测面部交换的DeepFake视频. 该方法实现了高精度,在Celeb-DF数据集上表现优于现有方法.
科学领域:
- 多媒体法医多媒体法医
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 对于多媒体法医而言,DeepFake视频检测至关重要.
- 换脸视频由于复杂的操纵技术而构成重大挑战.
研究的目的:
- 开发和验证一种检测面部交换视频的方法,使用已知个人的参考材料.
- 利用深度卷积神经网络 (DCNNs) 进行强大的面部识别和相似度评分.
主要方法:
- 使用值分类器,利用面部识别训练的DCNN的相似度得分.
- 从受访视频中的面孔和目标个体的参考图像之间计算了相似度得分.
- 最高的相似度得分被用来根据预先确定的值将视频分类为真实或假的.
主要成果:
- 该方法在Celeb-DF (v2) 数据集上实现了0.020的半总错误率 (HTER) 和0.994的曲线下面积 (AUC).
- 在同一数据集上,性能超过了现有的最先进的方法.
- 整合了后勤回归模型,将分数转换为法医应用的概率比.
结论:
- 提出的基于DCNN的方法对于检测面部交换的DeepFake视频非常有效.
- 与当前的强大方法相比,这种方法提供了更高的性能.
- 概率比率的整合提高了该方法在法医调查中的实用性.
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