用视频和微表达特征进行深度假冒检测的双分支融合模型.
Georgios Petmezas1, Vazgken Vanian1, Manuel Pastor Rufete2
1Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece.
Journal of imaging
|July 25, 2025
概括
这项研究引入了一种新的深度假冒检测方法,该方法结合了视频分析和面部微表情. 这种新的方法实现了近乎完美的准确性,显著改善了合成媒体检测.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数字法医学数字法医学
背景情况:
- 由于潜在的恶意应用,合成媒体或深度假冒的扩散带来了重大挑战.
- 现有的深度假冒检测方法经常与复杂的操纵作斗争,需要在检测准确性和稳定性方面取得进展.
研究的目的:
- 开发和验证一种新的深度假冒检测模型,将时空视频特征与微妙的面部微表情分析集成在一起.
- 通过利用互补的数据模式,提高深度假冒检测系统的准确性和可靠性.
主要方法:
- 设计了一种双分支的融合模型,采用3D ResNet18来从视频中提取时空特征.
- 使用变压器模型精心捕捉和分析面部微表情模式,这些模式本身很难准确地合成.
- 综合模型在全面的FaceForensics++ (FF++) 数据集上进行了严格的评估.
主要成果:
- 提出的深度假冒检测方法实现了99.81%的特殊准确率.
- 该模型获得了100%的完美接收器运行特征曲线下面区域 (ROC-AUC) 评分,表明了优越的区分能力.
- 性能基准表明,该新方法超过了当前最先进的深度假冒检测技术.
结论:
- 整合各种功能集,特别是时空视频数据和微表达力学,对于强大的深度假冒检测至关重要.
- 开发的双分支融合模型在打击合成介质的滥用方面取得了重大进展.
- 这项研究强调了混合方法在解决数字媒体操纵不断变化的格局方面的潜力.
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