The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Selection-channel-aware rich model for Steganalysis of digital images
Open accessAbstract
From the perspective of signal detection theory, it seems obvious that knowing the probabilities with which the individual cover elements are modified during message embedding (the so-called probabilistic slection channel) should improve steganalysis. It is, however, not clear how to incorporate this information into steganalysis features when the detector is built as a classifier. In this paper, we propose a variant of the popular spatial rich model (SRM) that makes use of the selection channel. We demonstrate on three state-of-the-art content-adaptive steganographic schemes that even an imprecise knowledge of the embedding probabilities can substantially increase the detection accuracy in comparison with feature sets that do not consider the selection channel. Overly adaptive embedding schemes seem to be more vulnerable than schemes that spread the embedding changes more evenly throughout the cover.
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Cites
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
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
Ensemble 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-citationUsing 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-citationEdge Adaptive Image Steganography Based on LSB Matching Revisited
Luo, W., Huang, F., & Huang, J. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2041812 · Cited by 661
Secure Steganographic Methods for Palette Images
Fridrich, J., & Du, R. (2000) · Lecture notes in computer science · doi:10.1007/10719724_4 · Cited by 121
Self-citationAdaptive steganalysis against WOW embedding algorithm
Tang, W., Li, H., Luo, W., & Huang, J. (2014) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/2600918.2600935 · Cited by 115
Cited by
Content-Adaptive Steganography by Minimizing Statistical Detectability
Sedighi, V., Cogranne, R., & Fridrich, J. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2486744 · Cited by 573
Self-citationDeep 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
Yedroudj-Net: An Efficient CNN for Spatial Steganalysis
Yedroudj, M., Comby, F., & Chaumont, M. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461438 · Cited by 303
Metadata
- Authors
- Denemark, T., Sedighi, V., Holub, V., Cogranne, R., & Fridrich, J.
- Venue
- 2014 IEEE International Workshop on Information Forensics and Security (WIFS)
- Publisher
- IEEE
- Published
- 2014-12
- Pages
- 48-53
- ISSN
- 2157-4774 (ISSN-L)
- OpenAlex
- W2538511122
- Cited by
- 375
- References
- 14
- FWCI
- 14.42
- Type
- conference paper
- Language
- en
- Open access
- green
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- Denemark, T., Sedighi, V., Holub, V., Cogranne, R., & Fridrich, J. (2014). Selection-channel-aware rich model for Steganalysis of digital images. 2014 IEEE International Workshop on Information Forensics and Security (WIFS). https://doi.org/10.1109/wifs.2014.7084302
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