实时移除对象改监控视频中的定位,采用YOLO-V8
1Department of Electronics & Communication Engineering, Jaypee Institute of Information & Technology, Noida, India.
Journal of forensic sciences
|April 4, 2024
概括
这项研究引入了一种新方法,用于检测监控视频中的物体移除,从而提高视频真实性. 该方法使用最大-中位数平均算法和YOLO-V8用于准确的伪造检测,这对于法律证据至关重要.
科学领域:
- 数字法医学数字法医学
- 计算机视觉 计算机视觉
- 多媒体安全 多媒体安全
背景情况:
- 视频内容受到高度信任,但在数字时代很容易被操纵.
- 确保视频真实性是研究人员面临的重大挑战.
- 在监控视频中检测物体移除伪造对于司法法医和法律证据至关重要.
研究的目的:
- 开发一种强大的方法来检测监控视频中的时空物体移除伪造.
- 提高法庭程序中的视频证据的可靠性.
- 利用深度学习来准确地定位造区域.
主要方法:
- 提出了一个被动的最大-中位数平均运动残余算法来揭示伪造痕迹.
- 使用YOLO-V8深度学习模型精确定位造区域.
- 在SYSU-OBJFORG数据集上训练了YOLO-V8网络,用于对象移除伪造检测.
主要成果:
- 提出的方法成功地揭示了可见的物体移除痕迹.
- 精心调整的YOLO-V8实现了0.99的F1得分和0.99的精度来分类和定位被改的区域.
- 该模型在界限框定位方面表现出高度的信心,表明可靠性.
结论:
- 开发的算法有效地检测到监控视频中的物体移除伪造.
- 微调的YOLO-V8模型是视频法医实时应用的可靠和高效的选择.
- 拟议的系统优于现有的深度学习方法,用于复杂的基于对象的视频伪造检测.
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