research-article

On dangers of overtraining steganography to incomplete cover model

Authors:

Jessica Fridrich,

Vojtech HolubAuthors Info & Claims

MM&Sec '11: Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security

Pages 69 - 76

https://doi.org/10.1145/2037252.2037266

Published: 29 September 2011 Publication History

Abstract

A modern direction in steganography calls for embedding while minimizing a distortion function defined in a sufficiently complex model space. In this paper we show that, quite surprisingly, even a high-dimensional cover model does not automatically guarantee immunity to simple attacks. Moreover, the security can be compromised if the distortion is optimized to an incomplete cover model. We demonstrate these pitfalls with two recently proposed steganographic schemes and support our arguments experimentally. Finally, we discuss how the corresponding models might be modified to eliminate the security flaws.

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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
Huang DLuo WLiu MTang WHuang J(2024)Steganography Embedding Cost Learning With Generative Multi-Adversarial NetworkIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.331893919(15-29)Online publication date: 2024
https://doi.org/10.1109/TIFS.2023.3318939
Rana KGoyal PSharma G(2024)Dual-branch convolutional neural network for robust camera model identificationExpert Systems with Applications10.1016/j.eswa.2023.121828238(121828)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121828
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Index Terms

On dangers of overtraining steganography to incomplete cover model
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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Information & Contributors

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Published In

cover image ACM Conferences

MM&Sec '11: Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security

September 2011

140 pages

ISBN:9781450308069

DOI:10.1145/2037252

General Chair:
Chad Heitzenrater
Air Force Research Lab, USA
,
Program Chairs:
Scott Craver
Binghamton University, USA
,
Jana Dittmann
Otto von Guericke University Magdeburg, Germany

Copyright © 2011 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 September 2011

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MM&Sec '11

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SIGMM

MM&Sec '11: Multimedia and Security Workshop

September 29 - 30, 2011

New York, Buffalo, USA

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Overall Acceptance Rate 128 of 318 submissions, 40%

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Citations

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
Huang DLuo WLiu MTang WHuang J(2024)Steganography Embedding Cost Learning With Generative Multi-Adversarial NetworkIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.331893919(15-29)Online publication date: 2024
https://doi.org/10.1109/TIFS.2023.3318939
Rana KGoyal PSharma G(2024)Dual-branch convolutional neural network for robust camera model identificationExpert Systems with Applications10.1016/j.eswa.2023.121828238(121828)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121828
Abdollahi BHarati ATaherinia A(2023)A review of Content Adaptive Image Steganography methodsSignal and Data Processing10.61186/jsdp.20.3.14120:3(141-182)Online publication date: 1-Dec-2023
https://doi.org/10.61186/jsdp.20.3.141
Hong ELim KOh TJang H(2023)Lightweight image steganalysis with block-wise pruningScientific Reports10.1038/s41598-023-43386-213:1Online publication date: 26-Sep-2023
https://doi.org/10.1038/s41598-023-43386-2
Muralidharan TCohen ACohen ANissim N(2022)The infinite race between steganography and steganalysis in imagesSignal Processing10.1016/j.sigpro.2022.108711201:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.sigpro.2022.108711
Deng XChen BLuo WLuo D(2022)Universal Image Steganalysis Based on Convolutional Neural Network with Global Covariance PoolingJournal of Computer Science and Technology10.1007/s11390-021-0572-037:5(1134-1145)Online publication date: 30-Sep-2022
https://doi.org/10.1007/s11390-021-0572-0
Liao XYin JChen MQin Z(2021)Adaptive Payload Distribution in Multiple Images Steganography Based on Image Texture FeaturesIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2020.3004708(1-1)Online publication date: 2021
https://doi.org/10.1109/TDSC.2020.3004708
Jang HOh TKim KRiess CSchirrmacher FAmerini IBestagini PPevny T(2020)Feature Aggregation Networks for Image SteganalysisProceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security10.1145/3369412.3395072(33-38)Online publication date: 22-Jun-2020
https://dl.acm.org/doi/10.1145/3369412.3395072
Sharifzadeh MAloraini MSchonfeld D(2020)Adaptive Batch Size Image Merging Steganography and Quantized Gaussian Image SteganographyIEEE Transactions on Information Forensics and Security10.1109/TIFS.2019.292944115(867-879)Online publication date: 2020
https://doi.org/10.1109/TIFS.2019.2929441
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