The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Content-Adaptive Steganography by Minimizing Statistical Detectability
Open accessAbstract
Most current steganographic schemes embed the secret payload by minimizing a heuristically defined distortion. Similarly, their security is evaluated empirically using classifiers equipped with rich image models. In this paper, we pursue an alternative approach based on a locally estimated multivariate Gaussian cover image model that is sufficiently simple to derive a closed-form expression for the power of the most powerful detector of content-adaptive least significant bit matching but, at the same time, complex enough to capture the non-stationary character of natural images. We show that when the cover model estimator is properly chosen, the state-of-the-art performance can be obtained. The closed-form expression for detectability within the chosen model is used to obtain new fundamental insight regarding the performance limits of empirical steganalysis detectors built as classifiers. In particular, we consider a novel detectability limited sender and estimate the secure payload of individual images.
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Citations
Cites
Testing Statistical Hypotheses
(2021) · Wiley Series in Probability and Statistics · doi:10.1002/9781119243830.ch12 · Cited by 5,106
Rich Models for Steganalysis of Digital Images
Fridrich, J., & Kodovsky, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190402 · Cited by 2,144
Self-citation”Break Our Steganographic System”: The Ins and Outs of Organizing BOSS
Bas, P., Filler, T., & Pevný, T. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-24178-9_5 · Cited by 1,250
Universal distortion function for steganography in an arbitrary domain
Holub, V., Fridrich, J., & Denemark, T. (2014) · EURASIP Journal on Information Security · doi:10.1186/1687-417x-2014-1 · Cited by 1,202
Self-citationEnsemble Classifiers for Steganalysis of Digital Media
Kodovsky, J., Fridrich, J., & Holub, V. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2175919 · Cited by 1,064
Self-citationScientific Charge-Coupled Devices
Janesick, J. R. (2001) · SPIE eBooks · doi:10.1117/3.374903 · Cited by 1,054
Using High-Dimensional Image Models to Perform Highly Undetectable Steganography
Pevný, T., Filler, T., & Bas, P. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-16435-4_13 · Cited by 1,009
Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes
Filler, T., Judas, J., & Fridrich, J. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2134094 · Cited by 966
Self-citationDesigning steganographic distortion using directional filters
Holub, V., & Fridrich, J. (2012) · 2012 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2012.6412655 · Cited by 966
Self-citationTesting Statistical Hypotheses
Neath, A. A. (2006) · Journal of the American Statistical Association · doi:10.1198/jasa.2006.s100 · Cited by 948
Cited by
Deep residual learning for image steganalysis
Wu, S., Zhong, S., & Liu, Y. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4440-4 · Cited by 442
Zhang, R., Zhu, F., Liu, J., & Liu, G. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2936913 · Cited by 420
A New Payload Partition Strategy in Color Image Steganography
Liao, X., Yu, Y., Li, B., Li, Z., & Qin, Z. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2896270 · Cited by 328
An Embedding Cost Learning Framework Using GAN
Yang, J., Ruan, D., Huang, J., Kang, X., & Shi, Y.-Q. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2922229 · Cited by 268
Metadata
- Authors
- Sedighi, V., Cogranne, R., & Fridrich, J.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2016-02
- Volume
- 11
- Issue
- 2
- Pages
- 221-234
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2192227561
- Cited by
- 573
- References
- 37
- FWCI
- 29.75
- Type
- journal article
- Language
- en
- Open access
- green
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