深度假视频欺骗检测使用基于视觉注意力的方法.
Kavita Lal1, Savita Shiwani1, Geeta Chhabra Gandhi2
1Poornima University, Jaipur, India.
Scientific reports
|November 18, 2025
概括
这项研究引入了一个具有视觉注意力的深度学习模型来检测深度假视频. 该模型有效地区分真实的内容和操纵的媒体,增强数字数据安全性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 数字法医学数字法医学
背景情况:
- 人工数字数据的扩散引发了人们对其真实性的担忧.
- 深度假冒技术,利用计算机视觉,通过使身份隐藏成为可能,构成重大威胁.
- 迫切需要可靠的方法来验证面部图像和视频的合法性.
研究的目的:
- 开发一种深度学习模型,能够区分真实和深度虚假的视觉内容.
- 提高数字媒体的安全性和可信度.
- 为了解决深假技术的滥用造成的社会焦虑.
主要方法:
- 开发了一个包含视觉注意力策略的深度学习模型.
- 从视频中提取了面部区域,并使用预训练的ResNeXt-50卷积神经网络 (CNN) 进行处理.
- 使用视觉注意力机制来检测深度假冒特定的文物.
主要成果:
- 拟议的模型在识别深度假冒内容方面表现出卓越的表现.
- 在交叉数据集条件下进行了评估,使用Face Forensic++ C23进行培训.
- 该模型在Celeb-DFv2和DFDC数据集上取得了强大的独立测试结果.
结论:
- 开发的具有视觉注意力的深度学习模型在检测深度假视频方面是有效的.
- 这种方法提供了一个强大的解决方案,用于验证面部媒体的真实性.
- 这些发现有助于打击恶意使用深度假冒技术并保护数字内容.
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