research-article

On completeness of feature spaces in blind steganalysis

Authors:

Jessica FridrichAuthors Info & Claims

MM&Sec '08: Proceedings of the 10th ACM workshop on Multimedia and security

Pages 123 - 132

https://doi.org/10.1145/1411328.1411352

Published: 22 September 2008 Publication History

Abstract

Blind steganalyzers can be used for many diverse applications in steganography that go well beyond a mere detection of stego content. A blind steganalyzer can also be used for constructing targeted attacks or as an oracle for designing steganographic methods. The feature space itself provides a low-dimensional model of covers useful for benchmarking. These applications require the feature space to be complete in the sense that the features fully characterize the space of covers. Incomplete feature sets may skew benchmarking scores and lead to poor steganalysis. As a simple test of completeness, we propose a general approach for constructing steganographic methods that approximately preserve the whole feature vector and thus become practically undetectable by any steganalyzer that uses the same feature set. We demonstrate the plausibility of this approach, which we call the Feature Correction Method (FCM) by constructing the FCM for a 274-dimensional feature set from a state-of-the-art blind steganalyzer for JPEG images.

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Cited By

Kombrink MGeradts ZWorring M(2024)Image Steganography Approaches and Their Detection Strategies: A SurveyACM Computing Surveys10.1145/369496557:2(1-40)Online publication date: 10-Oct-2024
https://dl.acm.org/doi/10.1145/3694965
Alinia MMitchell D(2024)Minimizing Distortion in Data Embedding Using LDGM Codes and the Cavity Method2024 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT57864.2024.10619127(226-231)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ISIT57864.2024.10619127
Lu WChen JZhang JHuang JWeng JZhou Y(2022)Secure Halftone Image Steganography Based on Feature Space and Layer EmbeddingIEEE Transactions on Cybernetics10.1109/TCYB.2020.302604752:6(5001-5014)Online publication date: Jun-2022
https://doi.org/10.1109/TCYB.2020.3026047
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Index Terms

On completeness of feature spaces in blind steganalysis
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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Published In

cover image ACM Conferences

MM&Sec '08: Proceedings of the 10th ACM workshop on Multimedia and security

September 2008

242 pages

ISBN:9781605580586

DOI:10.1145/1411328

General Chair:
Andrew Ker
University of Oxford, UK
,
Program Chairs:
Jana Dittmann
Otto-von-Guericke University, Magdeburg, Germany
,
Jessica Fridrich
SUNY Binghamton, USA

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 22 September 2008

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September 22 - 23, 2008

Oxford, United Kingdom

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Overall Acceptance Rate 128 of 318 submissions, 40%

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Cited By

Kombrink MGeradts ZWorring M(2024)Image Steganography Approaches and Their Detection Strategies: A SurveyACM Computing Surveys10.1145/369496557:2(1-40)Online publication date: 10-Oct-2024
https://dl.acm.org/doi/10.1145/3694965
Alinia MMitchell D(2024)Minimizing Distortion in Data Embedding Using LDGM Codes and the Cavity Method2024 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT57864.2024.10619127(226-231)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ISIT57864.2024.10619127
Lu WChen JZhang JHuang JWeng JZhou Y(2022)Secure Halftone Image Steganography Based on Feature Space and Layer EmbeddingIEEE Transactions on Cybernetics10.1109/TCYB.2020.302604752:6(5001-5014)Online publication date: Jun-2022
https://doi.org/10.1109/TCYB.2020.3026047
Lu WZhang JZhao XZhang WHuang J(2021)Secure Robust JPEG Steganography Based on AutoEncoder With Adaptive BCH EncodingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.302784331:7(2909-2922)Online publication date: Jul-2021
https://doi.org/10.1109/TCSVT.2020.3027843
Zhao XZhu JYu H(2020)On More Paradigms of SteganalysisCyber Warfare and Terrorism10.4018/978-1-7998-2466-4.ch045(723-740)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2466-4.ch045
Butora JYousfi YFridrich JRiess CSchirrmacher FAmerini IBestagini PPevny T(2020)Turning Cost-Based Steganography into Model-BasedProceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security10.1145/3369412.3395065(151-159)Online publication date: 22-Jun-2020
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Park JDoherty J(2019)A Steganographic Approach to Sonar TrackingIEEE Journal of Oceanic Engineering10.1109/JOE.2018.284716044:4(1213-1227)Online publication date: Oct-2019
https://doi.org/10.1109/JOE.2018.2847160
Zhao HRen JZhan JXiao YZhao SLei FAssaad MLi C(2019)Compressive sensing based secret signals recovery for effective image Steganalysis in secure communicationsMultimedia Tools and Applications10.1007/s11042-018-6065-778:20(29381-29394)Online publication date: 1-Oct-2019
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Wang ZQian ZZhang XYang MYe D(2018)On Improving Distortion Functions for JPEG SteganographyIEEE Access10.1109/ACCESS.2018.28841986(74917-74930)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2884198
Hemalatha JDevi MGeetha S(2018)Improving image steganalyser performance through curvelet transform denoisingCluster Computing10.1007/s10586-017-1500-5Online publication date: 8-Jan-2018
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