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.