Automatic estimation and compensation of geometric distortions in video copies The proliferation of pirate copies of feature films on peertopeer networks arouses a great interest to countermeasures such as the insertion of (invisible) forensic marks in projected movies, to deter their illegal capture. The registration of pirate copies with the original content is however a prerequisite to the recovery of such embedded messages, as severe geometric distortions often occur in illegally camcorded contents. After a brief stateoftheart in image registration, the paper details an algorithm for video registration, focusing on the compensation of geometric distortions. Control points are automatically extracted in original and copy pictures, followed by pre and postmatching filtering steps to discard not relevant control points and erroneous matched pairs of control points respectively. This enables the accurate numerical estimation of an 8parameter homographic distortion model, used to register the copy frames with the original reference grid. Such an image registration algorithm is inserted into a general video registration scheme. Results are presented on both natural and synthetic test material.

A Fast and Efficient Framework for Indexing and Detection of Modified Copies in Video In this paper, we propose a framework for detecting near duplicate copies of a video based on an ordinal method. The framework also incorporates a bitmap indexing structure instead of a typical indexing structure used in the previous published work. Using this method, two levels of indices are constructed. The first level of this process groups each input video (represented by their key frames) into k clusters. These clusters and the associated key frames are then used to construct the first level index. The second level of this process converts ordinalbased video signatures (generated using the technique developed in earlier work) into bitmap vectors. By adopting this twolevel indexing scheme, query processing times are significantly reduced. This is because, the system is required to match only videos in the clusters that are relevant to the query and not all the videos in the database. Additionally, the technique implemented utilizes a bitmap structure for indexing, resulting in less storage space. Furthermore, we are able to employ lowcost Boolean operations such as AND, OR, and XOR in the matching process instead of Euclidean distance or other similar matching algorithms. This helps to reduce the computational time for video matching. The system has demonstrated to effectively reduce the space needed to store collections of video signatures in a database, as well as improving the overall system performance. In addition, initial results show that the system is effective and robust to several transformations such as changes in brightness, color, contrast, resolution (reduction) as well as the addition of noise.