Cited By
View all- 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
Revisiting Perturbed Quantization
Pages 125 - 136
Abstract
In this work, we revisit Perturbed Quantization steganography with modern tools available to the steganographer today, including near-optimal ternary coding and content-adaptive embedding with side-information. In PQ, side-information in the form of rounding errors is manufactured by recompressing a JPEG image with a judiciously selected quality factor. This side-information, however, cannot be used in the same fashion as in conventional side-informed schemes nowadays as this leads to highly detectable embedding. As a remedy, we utilize the steganographic Fisher information to allocate the payload among DCT modes. In particular, we show that the embedding should not be constrained to contributing coefficients only as in the original PQ but should be expanded to the so-called "contributing DCT modes." This approach is extended to color images by slightly modifying the SI-UNIWARD algorithm. Using the best detectors currently available, it is shown that by manufacturing side information with double compression, one can embed the same amount of information into the doubly-compressed cover image with a significantly better security than applying J-UNIWARD directly in the single-compressed image. At the end of the paper, we show that double compression with the same quality makes side-informed steganography extremely detectable and should be avoided.
Supplementary Material
Presentation video
- Download
- 152.70 MB
References
[1]
H. Abdulrahman, M. Chaumont, P. Montesinos, and B. Magnier. Color image steganalysis using correlations between RGB channels. In Proceedings 10th International Conference on Availability, Reliability and Security (ARES), 4th International Workshop on Cyber Crime (IWCC), pages 448--454, Toulouse, France, August 24--28 2015.
[2]
H. Abdulrahman, M. Chaumont, P. Montesinos, and B. Magnier. Color image steganalysis using RGB channel geometric transformation measures. Wiley Journal on Security and Communication Networks, February 2016.
[3]
P. Bas, T. Filler, and T. Pevny. Break our steganographic system -- the ins and outs of organizing BOSS. In T. Filler, T. Pevny, A. Ker, and S. Craver, editors, Information Hiding, 13th International Conference, volume 6958 of Lecture Notes in Computer Science, pages 59--70, Prague, Czech Republic, May 18--20, 2011.
[4]
P. Bas and T. Furon. BOWS-2. http://bows2.ec-lille.fr, July 2007.
[5]
M. Boroumand, M. Chen, and J. Fridrich. Deep residual network for steganalysis of digital images. IEEE Transactions on Information Forensics and Security, 14(5):1181--1193, May 2019.
[6]
M. Boroumand and J. Fridrich. Synchronizing embedding changes in side-informed steganography. In Proceedings IS&T, Electronic Imaging, Media Water-marking, Security, and Forensics 2020, San Francisco, CA, January 26--30 2020.
[7]
S. Bozinovski and A. Fulgosi. The influence of pattern similarity and transfer learning upon training of a base perceptron b2. In Proceedings of Symposium Informatica 3--121--5, 1976.
[8]
J. Butora and J. Fridrich. Effect of jpeg quality on steganographic security. In R. Cogranne and L. Verdoliva, editors,The 7th ACM Workshop on Information Hiding and Multimedia Security, Paris, France, July 3--5, 2019. ACM Press.
[9]
J. Butora and J. Fridrich. Reverse JPEG compatibility attack. IEEE Transactions on Information Forensics and Security, 15:1444--1454, 2020.
[10]
J. Butora and J. Fridrich. Extending the reverse JPEG compatibility attack to double compressed images. In Proceedings IEEE, International Conference on Acoustics, Speech, and Signal Processing, Toronto, Canada, June 6--11, 2021.
[11]
J. Butora, Y. Yousfi, and J. Fridrich. How to pretrain for steganalysis. In The 9th ACM Workshop on Information Hiding and Multimedia Security, Brussels, Belgium, June 21--25, 2021.
[12]
K. Chubachi. An ensemble model using CNNs on different domains for ALASKA2image steganalysis. In IEEE International Workshop on Information Forensics and Security, New York, NY, December 6--11, 2020.
[13]
R. Cogranne. Selection-channel-aware reverse JPEG compatibility for highly reliable steganalysis of JPEG images. In Proceedings IEEE, International Conference on Acoustics, Speech, and Signal Processing, pages 2772--2776, Barcelona, Spain,May 4--8, 2020.
[14]
R. Cogranne, Q. Giboulot, and P. Bas. The ALASKA steganalysis challenge: A first step towards steganalysis "Into the wild". In R. Cogranne and L. Verdoliva, editors, The 7th ACM Workshop on Information Hiding and Multimedia Security, Paris, France, July 3--5, 2019. ACM Press.
[15]
R. Cogranne, Q. Giboulot, and P. Bas. ALASKA--2: Challenging academic research on steganalysis with realistic images. In IEEE International Workshop on Information Forensics and Security, New York, NY, December 6--11, 2020.
[16]
R. Cogranne, Q. Giboulot, and P. Bas. Steganography by minimizing statistical detectability: The cases of jpeg and color images. ACM Press, 2020.
[17]
J. L. Davidson and P. Parajape. Double-compressed JPEG detection in a steganalysis system. In Annual ADFSL Conference on Digital Forensics, Security, and Law, May 30, 2012.
[18]
T. Denemark, M. Boroumand, and J. Fridrich. Steganalysis features for content-adaptive JPEG steganography. IEEE Transactions on Information Forensics and Security, 11(8):1736--1746, August 2016.
[19]
T. Denemark and J. Fridrich. Side-informed steganography with additive distortion. In IEEE International Workshop on Information Forensics and Security, Rome, Italy, November 16--19 2015.
[20]
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei. ImageNet: A large-scale hierarchical image database. In IEEE conference on computer vision and pattern recognition, pages 248--255, June 20--25, 2009.
[21]
T. Filler, J. Judas, and J. Fridrich. Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Transactions on Information Forensics and Security, 6(3):920--935, September 2011.
[22]
J. Fridrich. On the role of side-information in steganography in empirical covers. In A. Alattar, N. D. Memon, and C. Heitzenrater, editors, Proceedings SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics 2013, volume 8665, pages 1--11, San Francisco, CA, February 5--7, 2013.
[23]
J. Fridrich, M. Goljan, and D. Soukal. Perturbed quantization steganography. ACM Multimedia System Journal, 11(2):98--107, 2005.
[24]
J. Fridrich, M. Goljan, D. Soukal, and P. Lisonk. Writing on wet paper. In T. Kalker and P. Moulin, editors,IEEE Transactions on Signal Processing, Special Issue on Media Security, volume 53, pages 3923--3935, October 2005. (journal version).
[25]
Q. Giboulot, R. Cogranne, and P. Bas. JPEG steganography with side information from the processing pipeline. In Proceedings IEEE, International Conference on Acoustics, Speech, and Signal Processing, pages 2767--2771, Barcelona, Spain, May 4--8, 2020.
[26]
Q. Giboulot, R. Cogranne, and P. Bas. Synchronization Minimizing Statistical Detectability for Side-Informed JPEG Steganography. In IEEE International Workshop on Information Forensics and Security, New York, NY, December 6--11, 2020.
[27]
L. Guo, J. Ni, and Y. Q. Shi. Uniform embedding for efficient JPEG steganography. IEEE Transactions on Information Forensics and Security, 9(5):814--825, May 2014.
[28]
V. Holub and J. Fridrich. Low-complexity features for JPEG steganalysis using undecimated DCT. IEEE Transactions on Information Forensics and Security, 10(2):219--228, February 2015.
[29]
V. Holub, J. Fridrich, and T. Denemark. Universal distortion design for steganography in an arbitrary domain. EURASIP Journal on Information Security, Special Issue on Revised Selected Papers of the 1st ACM IH and MMS Workshop, 2014:1, 2014.
[30]
X. Hu, J. Ni, W. Su, and J. Huang. Model-based image steganography using asymmetric embedding scheme. Journal of Electronic Imaging, 27(4):1 -- 7, 2018.
[31]
F. Huang, J. Huang, and Y.-Q. Shi. New channel selection rule for JPEG steganography. IEEE Transactions on Information Forensics and Security, 7(4):1181--1191, August 2012.
[32]
F. Huang, W. Luo, J. Huang, and Y.-Q. Shi. Distortion function designing for JPEG steganography with uncompressed side-image. In W. Puech, M. Chaumont, J. Dittmann, and P. Campisi, editors, 1st ACM IH&MMSec. Workshop, Montpellier, France, June 17--19, 2013.
[33]
J. Kodovsky and J. Fridrich. Calibration revisited. In J. Dittmann, S. Craver, and J. Fridrich, editors, Proceedings of the 11th ACM Multimedia & Security Workshop, pages 63--74, Princeton, NJ, September 7--8, 2009.
[34]
J. Kodovsky and J. Fridrich. Steganalysis of JPEG images using rich models. In A. Alattar, N. D. Memon, and E. J. Delp, editors, Proceedings SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics 2012, volume 8303, pages 0A1--13, San Francisco, CA, January 23--26, 2012.
[35]
J. Kodovsky, J. Fridrich, and V. Holub. Ensemble classifiers for steganalysis of digital media. IEEE Transactions on Information Forensics and Security, 7(2):432--444, April 2012.
[36]
S. Lai and R. Böhme. Block convergence in repeated transform coding: JPEG-100 forensics, carbon dating, and tamper detection. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 3028--3032, 2013.
[37]
T. Mingxing and V. L. Quoc. Efficient Net: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning, ICML, volume 97, pages 6105--6114, June 9--15, 2019.
[38]
S. Ozcan and A. F. Mustacoglu. Transfer learning effects on image steganalysis with pre-trained deep residual neural network model. In IEEE International Conference on Big Data (Big Data), pages 2280--2287, December 10--13, 2018.
[39]
W. Pennebaker and J. Mitchell.JPEG: Still Image Data Compression Standard. Van Nostrand Reinhold, New York, 1993.
[40]
X. Song, F. Liu, C. Yang, X. Luo, and Y. Zhang. Steganalysis of adaptive JPEG steganography using 2D Gabor filters. In P. Comesana, J. Fridrich, and A. Alattar, editors, 3rd ACM IH&MMSec. Workshop, Portland, Oregon, June 17--19, 2015.
[41]
T. Taburet, L. Filstroff, P. Bas, and W. Sawaya. An empirical study of steganography and steganalysis of color images in the JPEG domain. In International Workshop on Digital Forensics and Watermarking (IWDW), Jeju, South Korea, 2018.
[42]
G. Xu. Deep convolutional neural network to detect J-UNIWARD. In M. Stamm, M. Kirchner, and S. Voloshynovskiy, editors, The 5th ACM Workshop on Information Hiding and Multimedia Security, Philadelphia, PA, June 20--22, 2017.
[43]
M. Yedroudj, F. Comby, and M. Chaumont. Yedroudjnet: An efficient CNN for spatial steganalysis. In IEEE ICASSP, pages 2092--2096, Alberta, Canada, April 15--20, 2018.
[44]
Y. Yousfi, J. Butora, Q. Giboulot, and J. Fridrich. Breaking ALASKA: Color separation for steganalysis in JPEG domain. In R. Cogranne and L. Verdoliva, editors,The 7th ACM Workshop on Information Hiding and Multimedia Security, Paris, France, July 3--5, 2019. ACM Press.
[45]
Y. Yousfi, J. Butora, E. Khvedchenya, and J. Fridrich. Imagenet pre-trained cnns for jpeg steganalysis. In IEEE International Workshop on Information Forensics and Security, New York, NY, December 6--11, 2020.
[46]
Y. Zhou, W. W. Y. Ng, and Z. He. Effects of double jpeg compression on steganalysis. In International Conference on Wavelet Analysis and Pattern Recognition, pages 106--112, 2012.
[47]
F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He. A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1):43--76,2021.
Index Terms
- Revisiting Perturbed Quantization
Recommendations
A steganographic method based upon JPEG and quantization table modification
Special issue: Intelligent multimedia computing and networkingIn this paper, a novel steganographic method based on joint photographic expert-group (JPEG) is proposed. The proposed method modifies the quantization table first. Next, the secret message is hidden in the cover-image with its middle-frequency of the ...
Perturbed quantization steganography
In this paper, we use the recently proposed wet paper codes and introduce a new approach to passive-warden steganography called perturbed quantization. In perturbed quantization, the sender hides data while processing the cover object with an ...
Dither modulation based adaptive steganography resisting jpeg compression and statistic detection
In order to improve the JPEG compression resistant performance of the current steganogrpahy algorithms resisting statistic detection, an adaptive steganography algorithm resisting JPEG compression and detection based on dither modulation is proposed. ...
Comments
Information & Contributors
Information
Published In
June 2021
205 pages
ISBN:9781450382953
DOI:10.1145/3437880
- General Chairs:
- Dirk Borghys,
- Patrick Bas,
- Program Chairs:
- Luisa Verdoliva,
- Tomáš Pevný,
- Bin Li,
- Jennifer Newman
Copyright © 2021 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]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 June 2021
Check for updates
Badges
Best Paper
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
IH&MMSec '21
Sponsor:
IH&MMSec '21: ACM Workshop on Information Hiding and Multimedia Security
June 22 - 25, 2021
Virtual Event, Belgium
Acceptance Rates
Overall Acceptance Rate 128 of 318 submissions, 40%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 173Total Downloads
- Downloads (Last 12 months)51
- Downloads (Last 6 weeks)12
Reflects downloads up to 26 Sep 2024
Other Metrics
Citations
Cited By
View all- 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
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in