使用CoAtNet模型评估深度假视频中的特征和变化
Eman Alattas1,2, John Clark2, Arwa Al-Aama3
1Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Journal of imaging
|June 25, 2025
概括
CoAtNet模型显示出强大的深度假视频检测能力,在数据集内部和跨数据集评估中表现出色. 这种混合卷积变压器架构展示了用于识别操纵视频的卓越泛化.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 数字安全数字安全
背景情况:
- 深度假冒视频检测对于打击错误信息和加强数字安全至关重要.
- 先进的人工智能模型在各种数据集中的泛化能力尚未完全理解.
- CoAtNet是一种混合卷积变压器架构,在计算机视觉任务中表现有前途.
研究的目的:
- 评估CoAtNet模型在各种数据集中用于深度假视频检测的概括能力.
- 探索CoAtNet在跨数据集场景中的表现,识别深度假冒视频中的关键特征和变异.
- 在数据集内部和跨数据集深度假冒检测方面,将CoAtNet与最先进的模型进行基准测试.
主要方法:
- 使用CoAtNet模型进行了广泛的实验.
- 该模型使用各种输入和处理配置进行训练.
- 在公认的公开深度假冒数据集上评估了性能,包括Celeb-DF和DFDC.
主要成果:
- CoAtNet 在数据集内部实现了卓越的性能,曲线下的面积 (AUC) 从81.4%到99.9%不等.
- 该模型显示了强大的跨数据集概括性,达到78%的AUC.
- CoAtNet在数据集内部和跨数据集深度假冒检测方面表现出最佳的AUC,特别是在Celeb-DF上.
结论:
- CoAtNet 在深度假冒视频检测方面表现出卓越的概括能力.
- 该模型的混合架构有效地识别了不同数据集中的深度假冒.
- CoAtNet代表了强大的深度假冒检测技术的重大进步.
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