Video Steganalysis Based on Transform Domain

Abstract

Steganalysis, the method to detect steganographically embedded hidden messages in digital data, has received an increasing interest in recent years. Although significant research efforts have been devoted to develop steganalysis techniques for still-images, video steganalysis remains largely an explored area. This paper proposes a video steganalysis method to detect the presence of hidden messages. Features are extracted from the histograms of the wavelet subbands and the statistical moment of the wavelet characteristic functions Co-Occurrences, water-filling features. K-Nearest Neighbor (K-NN) is utilized as the classifier.