基于最大似然估计的自适应阈值视频被动取证

基于分块级的模式噪声，提出一种基于最大似然估计的自适应阈值视频被动取证方法.它采用小波去噪和维纳滤波提取传感器的模式噪声，并通过固定大小的滑动窗口，计算分块级的能量梯度、信噪比和相邻帧相同位置块模式噪声的相关性构造特征值向量.在此基础上，采用最大似然估计得到判别篡改区域的自适应阈值.仿真实验结果表明，提出的方法对于复制-粘贴的视频内容篡改取得了较好的取证效果，并且能够对较小区域的篡改进行定位.

A Maximum Likelihood Estimation-based Adaptive Threshold for Passive Video Forensics

Based on the block-level sensor pattern noise (SPN), a video forensics scheme, whose adaptive-threshold is obtained by maximum likelihood estimation, was proposed. It extracts the SPN by wavelet de-noising and Weiner filter. By setting a sliding window of fixed size, block-based energy gradient, signal-noise ratio and the correlation between the SPN of blocks with the same positions in neighboring frames are computed to build a feature vector. The maximum likelihood estimation is utilized to obtain the adaptive threshold of classification. Experiment results show that the proposed approach is effective for the forensics of copy-paste based tampering to the contents of digital video. Moreover, it can locate the tampering of small regions in digital video.