Steganalysis using color wavelet statistics and one-class support vector machines

Siwei Lyu; Hany Farid

doi:10.1117/12.526012

Steganalysis using color wavelet statistics and one-class support vector machines

Siwei Lyu
, Hany Farid

Department of Computer Science and Engineering

Dartmouth College

Research output: Contribution to journal › Conference article › peer-review

168 Scopus citations

Abstract

Steganographic messages can be embedded into digital images in ways that are imperceptible to the human eye. These messages, however, alter the underlying statistics of an image. We previously built statistical models using first-and higher-order wavelet statistics, and employed a non-linear support vector machines (SVM) to detect Steganographic messages. In this paper we extend these results to exploit color statistics, and show how a one-class SVM greatly simplifies the training stage of the classifier.

Original language	English
Pages (from-to)	35-45
Number of pages	11
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	5306
DOIs	https://doi.org/10.1117/12.526012
State	Published - 2004
Event	Security, Steganography, and Watermaking of Multimedia Contents VI - San Jose, CA, United States Duration: Jan 19 2004 → Jan 22 2004

Keywords

Steganalysis

Access to Document

10.1117/12.526012

Cite this

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abstract = "Steganographic messages can be embedded into digital images in ways that are imperceptible to the human eye. These messages, however, alter the underlying statistics of an image. We previously built statistical models using first-and higher-order wavelet statistics, and employed a non-linear support vector machines (SVM) to detect Steganographic messages. In this paper we extend these results to exploit color statistics, and show how a one-class SVM greatly simplifies the training stage of the classifier.",

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AU - Farid, Hany

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AB - Steganographic messages can be embedded into digital images in ways that are imperceptible to the human eye. These messages, however, alter the underlying statistics of an image. We previously built statistical models using first-and higher-order wavelet statistics, and employed a non-linear support vector machines (SVM) to detect Steganographic messages. In this paper we extend these results to exploit color statistics, and show how a one-class SVM greatly simplifies the training stage of the classifier.

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U2 - 10.1117/12.526012

DO - 10.1117/12.526012

M3 - Conference article

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SN - 0277-786X

VL - 5306

SP - 35

EP - 45

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Security, Steganography, and Watermaking of Multimedia Contents VI

Y2 - 19 January 2004 through 22 January 2004

ER -

Steganalysis using color wavelet statistics and one-class support vector machines

Abstract

Keywords

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