Selection of Rich Model Steganalysis Features Based on Decision Rough Set \alpha -Positive Region Reduction

• Published in: IEEE transactions on circuits and systems for video technology Vol. 29; no. 2; pp. 336 - 350
• Main Authors: Ma, Yuanyuan, Luo, Xiangyang, Li, Xiaolong, Bao, Zhenkun, Zhang, Yi
• Format: Journal Article
• Language: English
• Published: IEEE 01.02.2019
• Subjects: Adaptation models; Computational modeling; dimension reduction; Feature extraction; feature selection; Integrated circuit modeling; Principal component analysis; Rich Model; Rough sets; Steganalysis; Transform coding; α-positive region reduction
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2018.2799243

Steganography detection based on Rich Model features is a hot research direction in steganalysis. However, rich model features usually result a large computation cost. To reduce the dimension of steganalysis features and improve the efficiency of steganalysis algorithm, differing from previous works that normally proposed new feature extraction algorithm, this paper proposes a general steganalysis feature selection method based on decision rough set <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula>-positive region reduction. First, it is pointed out that decision rough set <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula>-positive region reduction is suitable for steganalysis feature selection. Second, a quantization method of attribute separability is proposed to measure the separability of steganalysis feature components. Third, steganalysis feature components selection algorithm based on decision rough set <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula>-positive region reduction is given; thus, stego images can be detected by the selected feature. The proposed method can significantly reduce the feature dimensions and maintain detection accuracy. Based on the BOSSbase-1.01 image database of 10 000 images, a series of feature selection experiments are carried on two kinds of typical rich model features (35263-D J+SRM feature and 17000-D GFR feature). The results show that even though these two kinds of features are reduced to approximately 8000-D, the detection performance of steganalysis algorithms based on the selected features are also maintained with that of original features, which will remarkably improve the efficiency of feature extraction and stego image detection.