通过多功能取证分析和1D卷积神经网络在汽车仪表盘视频中检测时间改
Ali Rehman Shinwari1,2, Uswah Binti Khairuddin1, Mohamad Fadzli Bin Haniff1
1Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种有效的方法来检测使用1D-CNN的仪表盘摄像头视频中的时间改. 该方法准确地识别了插入,删除和重复,提高了事故调查的视频真实性.
科学领域:
- 计算机视觉 计算机视觉
- 数字法医学数字法医学
- 机器学习 机器学习
背景情况:
- 汽车仪表板摄像头对于事故调查至关重要,但视频改对证据完整性构成重大风险.
- 现有的编辑工具有助于时间操作 (框架插入,删除,重复),需要强大的检测方法.
研究的目的:
- 开发一个计算效率高的框架来检测dashcam视频中的时间改.
- 为法律和保险目的提高视频证据的可靠性.
主要方法:
- 一个新的框架将高维的视频数据转化为紧的1D时间信号.
- 一个浅层的1D卷积神经网络 (1D-CNN) 被用来学习改模式.
- 五个互补的特征 (框架差异,SSIM漂移,光学流等) 在连续的之间提取.
主要成果:
- 实现了高检测精度:95.0%的删除,100.0%的插入,95.0%的重复在单次攻击设置中.
- 在四个类别的设置中 (非改,插入,删除,重复) 的总准确率为96.3%.
- 几乎实时的CPU推断 (≈12.7-12.9 FPS) 与最小的内存足迹.
结论:
- 拟议的多功能1D-CNN为时间改检测提供了一个实用,可解释和资源高效的解决方案.
- 这种方法支持智能运输系统中可靠的视频取证.
- 观察到域移动灵敏度,表明需要在域适应和增强方面进行进一步研究.
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