The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Universal distortion function for steganography in an arbitrary domain
Open accessAbstract
Currently, the most successful approach to steganography in empirical objects, such as digital media, is to embed the payload while minimizing a suitably defined distortion function. The design of the distortion is essentially the only task left to the steganographer since efficient practical codes exist that embed near the payload-distortion bound. The practitioner’s goal is to design the distortion to obtain a scheme with a high empirical statistical detectability. In this paper, we propose a universal distortion design called universal wavelet relative distortion (UNIWARD) that can be applied for embedding in an arbitrary domain. The embedding distortion is computed as a sum of relative changes of coefficients in a directional filter bank decomposition of the cover image. The directionality forces the embedding changes to such parts of the cover object that are difficult to model in multiple directions, such as textures or noisy regions, while avoiding smooth regions or clean edges. We demonstrate experimentally using rich models as well as targeted attacks that steganographic methods built using UNIWARD match or outperform the current state of the art in the spatial domain, JPEG domain, and side-informed JPEG domain.
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Vetterli, M., & Kovačević, J. (1995) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 2,898
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-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-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-citationSteganalysis by Subtractive Pixel Adjacency Matrix
Pevny, T., Bas, P., & Fridrich, J. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2045842 · Cited by 887
Self-citationSteganalysis by subtractive pixel adjacency matrix
Pevný, T., Bas, P., & Fridrich, J. (2009) · Proceedings of the 11th ACM workshop on Multimedia and security · doi:10.1145/1597817.1597831 · Cited by 769
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
Statistically undetectable jpeg steganography
Fridrich, J., Pevný, T., & Kodovský, J. (2007) · doi:10.1145/1288869.1288872 · Cited by 377
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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
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
CNN-Based Adversarial Embedding for Image Steganography
Tang, W., Li, B., Tan, S., Barni, M., & Huang, J. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2891237 · Cited by 292
Metadata
- Authors
- Holub, V., Fridrich, J., & Denemark, T.
- Venue
- EURASIP Journal on Information Security
- Published
- 2014-01-03
- Volume
- 2014
- Issue
- 1
- ISSN
- 1687-4161 (ISSN-L)
- 1687-417X
- OpenAlex
- W2170598445
- Cited by
- 1,202
- References
- 22
- FWCI
- 41.84
- Type
- journal article
- Language
- en
- Open access
- gold
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