With the rapid development of digital acquisition technology

media document are easily acquired and become an indispensable part of peoples' modern life. The powerful video editing software has made the video copy-paste forgery become more and more easy. Therefore

the appraisal of video authenticity has great significance. Direct extending the traditional image forgery detection algorithms to forgery video detection is computational expensive and time-consuming

moreover

the spatiotemporal consistency could not be preserved. In this paper

an intraframe copy-paste forgery video blind detection approach based on dense scale invariant feature transform(SIFT) flow is proposed. The proposed algorithm divides videos into sub-clips at the minimal content variation

extracts the keyframe as proxy frame. It detects the initial forgery region by matching SIFT keypoints

refines the forgery region by exploiting the SIFT key points dependence with mean shift region

and warps the keyframe detected region to the remaindering frames. The experiments showedthat the presented approach achieves one order of magnitude improvement in efficiency

and improves the mean detection accuracy by 20%. The proposed video forgery detection algorithm could efficiently detect the copy-paste forged regions within the video.