The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Using High-Dimensional Image Models to Perform Highly Undetectable Steganography
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Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Cited by
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
”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
Self-citationUniversal 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
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
Deep learning for steganalysis via convolutional neural networks
Qian, Y., Dong, J., Wang, W., & Tan, T. (2015) · SPIE Proceedings · doi:10.1117/12.2083479 · Cited by 537
Metadata
- Authors
- Pevný, T., Filler, T., & Bas, P.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2010
- Pages
- 161-177
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1955882805
- Cited by
- 1,009
- References
- 1
- FWCI
- 21.72
- Type
- conference paper
- Language
- en
- Open access
- green
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- Pevný, T., Filler, T., & Bas, P. (2010). Using High-Dimensional Image Models to Perform Highly Undetectable Steganography. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-16435-4_13
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
