https://www.shsu.edu/~qxl005/New/Publications/PR_lsbmatching.pdf

Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images

Image complexity and feature mining for steganalysis of least significant bit matching steganography

In this paper, we present a scheme based on feature mining and neuro-fuzzy inference system for detecting LSB matching steganography in grayscale images, which is a very challenging problem in steganalysis. Four types of features are proposed, and a Dynamic Evolving Neural Fuzzy Inference System (DENFIS) based feature selection is proposed, as well as the use of Support Vector Machine Recursive Feature Elimination (SVM-RFE) to obtain better detection accuracy. In comparison with other well-known features, overall, our features perform the best. DENFIS outperforms some traditional learning classifiers. SVM-RFE and DENFIS based feature selection outperform statistical significance based feature selection such as t-test. Experimental results also indicate that it remains very challenging to steganalyze LSB matching steganography in grayscale images with high complexity.

/pdf/feature-mining-and-neuro-fuzzy-inference-system-for-2zgwi01hxm.pdf

Feature mining and neuro-fuzzy inference system for steganalysis of LSB matching stegangoraphy in grayscale images

(NMT) in 2002. He is a great mentor with broad and long view and continuous scientific passion. I truly appreciate his advice and guidance in my research and his generous support throughout my PhD study as well as his encouraging and affording me to attend the prestigious international academic conferences, and hence broaden my horizon and make me thriving. Without him, I could not have this opportunity of pursuing my Ph.D. at the NMT of U.S.A., let alone the finish of this thesis and my growth up in the past five years. who have provided assistance and guidance during my PhD study. They also gave me helpful suggestions on my study. I am very grateful for their help. taught me the knowledge of several subjects in computer science. I got much expertise and knowledge from them since I was their Teaching Assistant. iii I thank Drs. Lyu, Simoncelli and Harmsen for providing me their codes which are used in the comparison study in steganalysis (chapters 2, 3, and 4) and Drs. and Diaz-Uriarte for sending me or publicizing their codes which are used in the comparison study in Microarray analysis (chapter 6). Special thank goes to Dr. Liang for her insightful discussion and offering me the SNP data sets which are tested in chapter 7. The friendship of Dr. Jianyun Xu and Dr. Zhongxue Chen is highly appreciated and has led to many interesting and good-spirited discussions related to this research. Without a shadow of doubt, my English tutors Lisa, Kay, and Jonathan did very good job in helping me to improve my English. I appreciate them for their very helpful tutoring, great patience, precious time, and love from the bottom of my heart. Last, but not least, I would acknowledge my family for their understanding and love during the past years. Their support, encouragement, passion, and love were in the end what made this dissertation possible. ABSTRACT Steganalysis aims to detect the information-hiding behavior in steganographic systems. Bioinformatics is to solve the biological problems usually on the molecular level. Although steganalysis and bioinformatics are completely different, both of them involve feature mining and computational intelligence techniques. It is very challenging to solve the problems in these two fields. In this thesis, chapter 1 is the introduction on steganography and steganalysis and chapter 5 is the introduction on bioinformatics; the contributions in image steganalysis are presented in chapters 2, 3, …

Vignesh Venkataramana

Papers

Detect JPEG Steganography Using Polynomial Fitting