Detection And Localization Of Splicing Attacks On Videos Using Block Correlation Temporal attacks (such as copymove and splicing attacks) are some of the most common tampering techniques in digital video due to their simplicity. While copymove attacks have been widely studied and different approaches for this task have been proposed in recent years, splicing attacks have been less studied and the works on splicing detection are almost only based on exploiting the sensor pattern noise. This work proposes a straightforward yet effective approach to detect splicing attacks on videos using peak detection over temporal correlation of blocks. This approach allows to detect both the spatial and temporal location of the splicing attacks even when the two videos used in the attack were recorded with the same camera, a case that cannot be addressed with those approaches based on noise patterns.

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.