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.
Description:
This research was supported by SDRC (Software
Development and Research Center) in University of
Computer Studies, Yangon. SDRC is a Research Center
designated by Myanmar Science and Engineering
Foundation and Ministry of Science & Technology.
