research-article

Fighting the Reverse JPEG Compatibility Attack: Pick your Side

Authors:

Patrick BasAuthors Info & Claims

IH&MMSec '22: Proceedings of the 2022 ACM Workshop on Information Hiding and Multimedia Security

Pages 113 - 121

https://doi.org/10.1145/3531536.3532955

Published: 23 June 2022 Publication History

Abstract

In this work we aim to design a steganographic scheme undetectable by the Reverse JPEG Compatibility Attack (RJCA). The RJCA, while only effective for JPEG images compressed with quality factors 99 and 100, was shown to work mainly due to change in variance of the rounding errors after decompression of the DCT coefficients, which is induced by embedding changes incompatible with the JPEG format. One remedy to preserve the aforementioned format is utilizing during the embedding the rounding errors created during the JPEG compression, but no steganographic method is known to be resilient to RJCA without this knowledge. Inspecting the effect of embedding changes on variance and also mean of decompression rounding errors, we propose a steganographic method allowing resistance against RJCA without any side-information. To resist RJCA, we propose a distortion metric making all embedding changes within a DCT block dependent, resulting in a lattice-based embedding. Then it turns out it is enough to cleverly pick the side of the (binary) embedding changes through inspection of their effect on the variance of decompression rounding errors and simply use uniform costs in order to enforce their sparsity across DCT blocks. To increase security against detectors in the spatial (pixel) domain, we show an easy way of combining the proposed methodology with steganography designed for spatial domain security, further improving the undetectability for quality factor 99. The improvements over existing non-informed steganography are up to 40% in terms of detector's accuracy.

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Cited By

Butora JBas P(2024)Size-Independent Reliable CNN for RJCA SteganalysisIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.337984919(4420-4431)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3379849
Butora JBas P(2023)Side-Informed Steganography for JPEG Images by Modeling Decompressed ImagesIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.326888418(2683-2695)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIFS.2023.3268884
Sachnev VSundararajan NSuresh S(2023)A New Approach for JPEG Steganalysis with a Cognitive Evolving Ensembler and Robust Feature SelectionCognitive Computation10.1007/s12559-022-10087-315:2(751-764)Online publication date: 9-Jan-2023
https://doi.org/10.1007/s12559-022-10087-3

Index Terms

Fighting the Reverse JPEG Compatibility Attack: Pick your Side
1. Computing methodologies
  1. Computer graphics
    1. Image compression
2. Security and privacy

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Published In

cover image ACM Conferences

IH&MMSec '22: Proceedings of the 2022 ACM Workshop on Information Hiding and Multimedia Security

June 2022

177 pages

ISBN:9781450393553

DOI:10.1145/3531536

General Chair:
B.S. Manjunath
UC Santa Barbara, California, USA
,
Program Chairs:
Jan Butora
University of Lille, France
,
Benedetta Tondi
University of Siena, Italy
,
Claus Vielhauer
Brandenburg University of Applied Sciences, Germany

Copyright © 2022 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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New York, NY, United States

Publication History

Published: 23 June 2022

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IH&MMSec '22

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IH&MMSec '22: ACM Workshop on Information Hiding and Multimedia Security

June 27 - 28, 2022

CA, Santa Barbara, USA

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Cited By

Butora JBas P(2024)Size-Independent Reliable CNN for RJCA SteganalysisIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.337984919(4420-4431)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3379849
Butora JBas P(2023)Side-Informed Steganography for JPEG Images by Modeling Decompressed ImagesIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.326888418(2683-2695)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIFS.2023.3268884
Sachnev VSundararajan NSuresh S(2023)A New Approach for JPEG Steganalysis with a Cognitive Evolving Ensembler and Robust Feature SelectionCognitive Computation10.1007/s12559-022-10087-315:2(751-764)Online publication date: 9-Jan-2023
https://doi.org/10.1007/s12559-022-10087-3

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