USING EXPANDED MARKOV PROCESS AND JOINT DISTRIBUTION FEATURES FOR JPEG STEGANALYSIS

Qingzhong Liu, Andrew H. Sung, Mengyu Qiao, Bernardete M. Ribeiro

2009

Abstract

In this paper, we propose a scheme for detecting the information-hiding in multi-class JPEG images by combining expanded Markov process and joint distribution features. First, the features of the condition and joint distributions in the transform domains are extracted (including the Discrete Cosine Transform or DCT, the Discrete Wavelet Transform or DWT); next, the same features from the calibrated version of the testing images are extracted. A Support Vector Machine (SVM) is applied to the differences of the features extracted from the testing image and from the calibrated version. Experimental results show that this approach delivers good performance in identifying several hiding systems in JPEG images.

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Paper Citation


in Harvard Style

Liu Q., H. Sung A., Qiao M. and Ribeiro B. (2009). USING EXPANDED MARKOV PROCESS AND JOINT DISTRIBUTION FEATURES FOR JPEG STEGANALYSIS . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 226-231. DOI: 10.5220/0001658402260231

in Bibtex Style

@conference{icaart09,
author={Qingzhong Liu and Andrew H. Sung and Mengyu Qiao and Bernardete M. Ribeiro},
title={USING EXPANDED MARKOV PROCESS AND JOINT DISTRIBUTION FEATURES FOR JPEG STEGANALYSIS},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2009},
pages={226-231},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001658402260231},
isbn={978-989-8111-66-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - USING EXPANDED MARKOV PROCESS AND JOINT DISTRIBUTION FEATURES FOR JPEG STEGANALYSIS
SN - 978-989-8111-66-1
AU - Liu Q.
AU - H. Sung A.
AU - Qiao M.
AU - Ribeiro B.
PY - 2009
SP - 226
EP - 231
DO - 10.5220/0001658402260231