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What makes the stego image undetectable?

Published: 19 August 2015 Publication History

Abstract

Steganography is the art of hiding information in ways that prevents the detection of hidden messages. Image steganography, which hides messages into a cover image for secret transmission, attracts increasing attention in social media era. Currently, most works focus on designing message embedding algorithms to avoid the stego images being distinguished from normal ones via visual observation or statistical analysis. This paper aims to make the detection of the stego images more difficult by selecting the suitable cover images. We propose a new measure to evaluate the hiding ability of the cover image based on Fisher Information Matrix and Gaussian Mixture Model. Experiments on standard dataset validate that the cover image with good hiding ability can improve the performance of various steganography algorithms obviously. Moreover, the proposed measure provides a statistical explanation of the existing cover image selection techniques and shows better performance against steganalysis.

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Cited By

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  • (2025)DYST (Did You See That?): An Amplified Covert Channel That Points To Previously Seen DataIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.341067922:1(614-631)Online publication date: Jan-2025
  • (2024)Improving Steganographic Security with Source BiasingProceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security10.1145/3658664.3659646(19-30)Online publication date: 24-Jun-2024
  • (2024)Cover Selection in Encrypted ImagesIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.344791334:12(13626-13641)Online publication date: Dec-2024
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Published In

cover image ACM Other conferences
ICIMCS '15: Proceedings of the 7th International Conference on Internet Multimedia Computing and Service
August 2015
397 pages
ISBN:9781450335287
DOI:10.1145/2808492
  • General Chairs:
  • Ramesh Jain,
  • Shuqiang Jiang,
  • Program Chairs:
  • John Smith,
  • Jitao Sang,
  • Guohui Li
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 August 2015

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Author Tags

  1. Fisher information
  2. Gaussian mixture model
  3. cover image selection
  4. steganography

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  • Research-article

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ICIMCS '15

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ICIMCS '15 Paper Acceptance Rate 20 of 128 submissions, 16%;
Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

View all
  • (2025)DYST (Did You See That?): An Amplified Covert Channel That Points To Previously Seen DataIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.341067922:1(614-631)Online publication date: Jan-2025
  • (2024)Improving Steganographic Security with Source BiasingProceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security10.1145/3658664.3659646(19-30)Online publication date: 24-Jun-2024
  • (2024)Cover Selection in Encrypted ImagesIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.344791334:12(13626-13641)Online publication date: Dec-2024
  • (2022)Cover Selection for Steganography Using Image SimilarityIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.3181039(1-13)Online publication date: 2022
  • (2021)Batch Steganography via Generative NetworkIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.297488431:1(88-97)Online publication date: 1-Jan-2021
  • (2021)Cover selection technique for secure transform domain image steganographyIran Journal of Computer Science10.1007/s42044-020-00077-94:4(241-252)Online publication date: 7-Jan-2021
  • (2021)Enhancing visual quality of spatial image steganography using SqueezeNet deep learning networkMultimedia Tools and Applications10.1007/s11042-021-11315-y80:28-29(36093-36109)Online publication date: 1-Nov-2021
  • (2020)Practical Cover Selection for SteganographyIEEE Signal Processing Letters10.1109/LSP.2019.295641627(71-75)Online publication date: 2020
  • (2019)Towards Improved Steganalysis: When Cover Selection is Used in SteganographyIEEE Access10.1109/ACCESS.2019.29551137(168914-168921)Online publication date: 2019
  • (2019)Secure Cover Selection for SteganographyIEEE Access10.1109/ACCESS.2019.29142267(57857-57867)Online publication date: 2019
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