A novel video copy detection method based on statistical analysis

The careless illegally copied contents have been rising serious social problem as Internet and multimedia technologies are developing. Therefore, effective and efficient copy detection techniques are required for content management and rights protection. In this paper, we propose a content-based hierarchical video copy detection method that estimates similarity using statistical characteristics between an original video and its spatial variations. We rank luminance average value of video that is robust to the special transformation, and choose similar video sequences named as candidate segments in huge amount of database to reduce processing time and complexity. Finally, we detect the copied video by using the hypothesis test of mean. As experiment result, proposed method has similar copy detection ratio accuracy to the reference method while our processing time and complexity are less than those of the reference since we can reduce the number of keyframes up to 50%. Also, the proposed method can efficiently detect spatial variations such as blur, contrast change, zoom in, and zoom out.