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Passive steganalysis based on higher order image statistics of curvelet transform

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Abstract

Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye. This paper presents a novel passive steganalysis strategy in which the task is approached as a pattern classification problem. A critical part of the steganalyser design depends on the selection of informative features. This paper is aimed at proposing a novel attack with improved performance indices with the following implications: 1) employing higher order statistics from a curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images, as compared to other conventional wavelet transforms; 2) increasing the sensitivity and specificity of the system by the feature reduction phase; 3) realizing the system using an efficient classification engine, a neuro-C4.5 classifier, which provides better classification rate. An extensive experimental evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.

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Authors and Affiliations

  1. Department of Information Technology, Thiagarajar College of Engineering, Madurai, 625 015, Tamil Nadu, India

    S. Geetha & Siva S. Sivatha Sindhu

  2. Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, 625 015, Tamil Nadu, India

    N. Kamaraj

Authors
  1. S. Geetha
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  2. Siva S. Sivatha Sindhu
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  3. N. Kamaraj
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Corresponding author

Correspondence to S. Geetha.

Additional information

S. Geetha received the B. Eng. and M. Eng. degrees in computer science and engineering in 2000 and 2004, respectively, from the Madurai Kamaraj University and Anna University of Chennai, India. In July 2004, she joined the Department of Information Technology at Thiagarajar College of Engineering, Madurai, India. She is a recipient of University Rank and Academic Topper Award in B.Eng. and M. Eng. in 2000 and 2004, respectively. She was an editor for Proceedings the 1st Indian Conference on Computational Intelligence and Information Security.

Her research interests include multimedia security, intrusion detection systems, machine learning paradigms, and information forensics.

Siva S. Sivatha Sindhu received the B.Eng. and M.Eng. degrees in computer science and engineering from Maharaja Sayajirao University of Baroda and Anna University, India in 2002 and 2004, respectively. She is currently with the Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamilnadu, India.

Her research interests include information security, intrusion detection systems, and soft computing approaches.

N. Kamaraj received the B.Eng. degree in electrical and electronics engineering and the M.Eng. degree in power system engineering from Madurai Kamaraj University, India in 1988 and 1994, respectively. He received the Ph.D. degree in the power system security assessment in 2003 from Madurai Kamaraj University. He is an associate professor in Electrical Engineering Department, Thiagarajar College of Engineering, Madurai, Tamilnadu, India. Currently, he is heading the Department of Electrical and Electronics Engineering in Thiagarajar College of Engineering. He is the recipient of Merit Award from IEEE Computer Society for Computer Society International Design Competition (CSIDC) 2003 as best advisor for the team contested in CSIDC. Also, he has received Gold Medal and Corps Subject Award from Institution of Engineers (India) for the year 2003.

His research interests include security assessment using neural network, fuzzy logic, and genetic algorithm.

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Geetha, S., Sivatha Sindhu, S.S. & Kamaraj, N. Passive steganalysis based on higher order image statistics of curvelet transform. Int. J. Autom. Comput. 7, 531–542 (2010). https://doi.org/10.1007/s11633-010-0537-1

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