To fast and accurately detect videos that were tampered

the human visual model is introduced to our algorithm. A multi-level and multi-granularity algorithm to detect and locate video tampering is presented in this paper. The random block sampling technology is used

and video structure perceptual features and time-domain perceptual features of video images are extracted. Then

the unidirectional abstract of the hash theory is used to quantify perceptual features

and the video abstract hash value is obtained. The similarity matrix is applied to give a multi-level and multi-granularity detection and location for tampered data. Experimental results show that similarity fitting diagram can reflect the attack power and the attack site of video tampering. The proposed algorithm shows better precision and positioning accuracy.