Steganalysis of LSB-matching steganography by removing most significant bit planes

M. A. Mehrabi; H. Aghaeinia; M. Abolghasemi

doi:10.1109/ISTEL.2008.4651396

Steganalysis of LSB-matching steganography by removing most significant bit planes

M. A. Mehrabi, H. Aghaeinia, M. Abolghasemi

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

2 Citations (Scopus)

Abstract

![CDATA[This paper proposed a new Steganalysis scheme of LSB-Matching steganography based on statistical moments of the DFT of histogram of multi-level wavelet subbands. Before deriving these wavelet subbands a pre-processing apply to images under the test. The pre-processing contains removing some most significant bit planes. Then we decompose the image using three-level Haar discrete wavelet transform (DWT) into 13 subbands (here the image itself is considered as the LL0 subband).The Fourier transform of each subband histogram, is calculated. Then it is divided into low and high frequency bands. The first three statistical moments of each band are selected to form a 78- dimensional feature vector for Steganalysis. Support Vector Machines (SVM) classifier is then used to discriminate between stego-images and innocent images.]]

Original language	English
Title of host publication	Proceedings of the 2008 International Symposium on Telecommunications, Tehran, Iran, 27-28 August 2008
Publisher	IEEE
Pages	731-734
Number of pages	4
ISBN (Print)	9781424427505
DOIs	https://doi.org/10.1109/ISTEL.2008.4651396
Publication status	Published - 2008
Event	International Symposium on Telecommunications - Duration: 4 Dec 2010 → …

Conference

Conference	International Symposium on Telecommunications
Period	4/12/10 → …

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10.1109/ISTEL.2008.4651396

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@inproceedings{14835167dda14fbcac0d74d861e8b22b,

title = "Steganalysis of LSB-matching steganography by removing most significant bit planes",

abstract = "![CDATA[This paper proposed a new Steganalysis scheme of LSB-Matching steganography based on statistical moments of the DFT of histogram of multi-level wavelet subbands. Before deriving these wavelet subbands a pre-processing apply to images under the test. The pre-processing contains removing some most significant bit planes. Then we decompose the image using three-level Haar discrete wavelet transform (DWT) into 13 subbands (here the image itself is considered as the LL0 subband).The Fourier transform of each subband histogram, is calculated. Then it is divided into low and high frequency bands. The first three statistical moments of each band are selected to form a 78- dimensional feature vector for Steganalysis. Support Vector Machines (SVM) classifier is then used to discriminate between stego-images and innocent images.]]",

author = "Mehrabi, {M. A.} and H. Aghaeinia and M. Abolghasemi",

year = "2008",

doi = "10.1109/ISTEL.2008.4651396",

language = "English",

isbn = "9781424427505",

pages = "731--734",

booktitle = "Proceedings of the 2008 International Symposium on Telecommunications, Tehran, Iran, 27-28 August 2008",

publisher = "IEEE",

note = "International Symposium on Telecommunications ; Conference date: 04-12-2010",

}

TY - GEN

T1 - Steganalysis of LSB-matching steganography by removing most significant bit planes

AU - Mehrabi, M. A.

AU - Aghaeinia, H.

AU - Abolghasemi, M.

PY - 2008

Y1 - 2008

N2 - ![CDATA[This paper proposed a new Steganalysis scheme of LSB-Matching steganography based on statistical moments of the DFT of histogram of multi-level wavelet subbands. Before deriving these wavelet subbands a pre-processing apply to images under the test. The pre-processing contains removing some most significant bit planes. Then we decompose the image using three-level Haar discrete wavelet transform (DWT) into 13 subbands (here the image itself is considered as the LL0 subband).The Fourier transform of each subband histogram, is calculated. Then it is divided into low and high frequency bands. The first three statistical moments of each band are selected to form a 78- dimensional feature vector for Steganalysis. Support Vector Machines (SVM) classifier is then used to discriminate between stego-images and innocent images.]]

AB - ![CDATA[This paper proposed a new Steganalysis scheme of LSB-Matching steganography based on statistical moments of the DFT of histogram of multi-level wavelet subbands. Before deriving these wavelet subbands a pre-processing apply to images under the test. The pre-processing contains removing some most significant bit planes. Then we decompose the image using three-level Haar discrete wavelet transform (DWT) into 13 subbands (here the image itself is considered as the LL0 subband).The Fourier transform of each subband histogram, is calculated. Then it is divided into low and high frequency bands. The first three statistical moments of each band are selected to form a 78- dimensional feature vector for Steganalysis. Support Vector Machines (SVM) classifier is then used to discriminate between stego-images and innocent images.]]

UR - https://hdl.handle.net/1959.7/uws:70790

U2 - 10.1109/ISTEL.2008.4651396

DO - 10.1109/ISTEL.2008.4651396

M3 - Conference Paper

SN - 9781424427505

SP - 731

EP - 734

BT - Proceedings of the 2008 International Symposium on Telecommunications, Tehran, Iran, 27-28 August 2008

PB - IEEE

T2 - International Symposium on Telecommunications

Y2 - 4 December 2010

ER -

Steganalysis of LSB-matching steganography by removing most significant bit planes

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