Video Steganalysis Using Histogram and Texture Features

Abstract

Information hiding in video streams with the development of network and multimedia technologies has played an important role in the steganographical field, and correspondingly video stegnanlysis techniques are catching attention of the security d epartment of each government. This paper presents an improved steganalysis technique to detect the presence of hidden me ssages. In order to identify and classify the two types of feature are used. The first type feature is texture feature that is computed by statistical, p sychological and signal processing The second type feature is the state of the art histogram based features This system is classified by using a Support Vector Machine ( SVM Classification is performed between actual video frames, steganograpyh frames from MSU u nique tool for hiding information in video) allows hiding text file in a video sequence. SVM is excellent classifier for two class problem that give higher detection accuracy rate for this system. Experimental results show that the proposed scheme can effectively detect whether a video has been processed by stego or not.