Blind steganalysis method for JPEG steganography combined with the semisupervised learning and soft margin support vector machine

Abstract

Stego images embedded by unknown steganographic algorithms currently may not be detected by using steganalysis detectors based on binary classifier. However, it is difficult to obtain high detection accuracy by using universal steganalysis based on one-class classifier. For solving this problem, a blind detection method for JPEG steganography was proposed from the perspective of information theory. The proposed method combined the semisupervised learning and soft margin support vector machine with steganalysis detector based on one-class classifier to utilize the information in test data for improving detection performance. Reliable blind detection for JPEG steganography was realized only using cover images for training. The experimental results show that the proposed method can contribute to improving the detection accuracy of steganalysis detector based on one-class classifier and has good robustness under different source mismatch conditions.