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2018 | OriginalPaper | Chapter

A New Universal Steganalyzer for JPEG Images

Authors : Ge Liu, Fangjun Huang, Qi Chen, Zhonghua Li

Published in: Machine Learning and Intelligent Communications

Publisher: Springer International Publishing

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Abstract

The JPEG (Joint Photographic Experts Group) file format is currently one of the most widely used image formats. The study on JPEG steganography and steganalysis is a hotspot in the field of information hiding. With the matrix coding and some new adaptive embedding strategies having been put forward, the detection of stego images is becoming more and more difficult. In recent years, a series of new feature extraction methods have been proposed in the field of steganalysis. However, the detection accuracy rate can only be increased by 1–2% points or even less. Based on those existing steganalytic algorithms, a new feature merging method is proposed in this paper. Via merging features extracted from different domains, the detection accuracy rate of those existing JPEG steganalytic algorithms can be improved by 3% points or even higher. Considering about that the feature dimension is so high after feature merging and thus it may bring difficulties in the feature extraction, training and classification process, a new feature selection method is also proposed in this paper. Experimental results demonstrate that it can not only achieve reduction of the dimensionality, but also maintain a high detection accuracy rate.

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Title: A New Universal Steganalyzer for JPEG Images
Authors: Ge Liu
Fangjun Huang
Qi Chen
Zhonghua Li
Publisher: Springer International Publishing
Book: Machine Learning and Intelligent Communications
Print ISBN: 978-3-319-73563-4

Electronic ISBN: 978-3-319-73564-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-73564-1_15

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