SVM Classification with Steganography using Video Copy Detection based on Network Security with Machine Learning Framework

Its incomplete duplicate detection in the videos problem aimed to identify whether each or many segments of a request video are already present in this study, even while giving data on related fraction time periods. At the moment, the most efficient incomplete copy detection techniques in videos have been designed in 3 components: extraction of features, pattern matching, as well as time integration. To those level, the isolation of the feature identification but also time alignment modules ignores the spatially information of an incomplete copy. In order to decrease the above loss, we begin by representing video frames but also extracting SVM features. As a result, in this paper, we examine the algorithm's performance on the particularly complex video duplicate recognition data-set VCDB, that also, once again, outperforms the government's incomplete copy detection method.