The DOI matches its Crossref record; 37 of the 38 references it cites were found in a registry or an index.
Rich Models for Steganalysis of Digital Images
Abstract
We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters. In contrast to previous approaches, we make the model assembly a part of the training process driven by samples drawn from the corresponding cover- and stego-sources. Ensemble classifiers are used to assemble the model as well as the final steganalyzer due to their low computational complexity and ability to efficiently work with high-dimensional feature spaces and large training sets. We demonstrate the proposed framework on three steganographic algorithms designed to hide messages in images represented in the spatial domain: HUGO, edge-adaptive algorithm by Luo, and optimally coded ternary$\pm {\hbox{1}}$embedding. For each algorithm, we apply a simple submodel-selection technique to increase the detection accuracy per model dimensionality and show how the detection saturates with increasing complexity of the rich model. By observing the differences between how different submodels engage in detection, an interesting interplay between the embedding and detection is revealed. Steganalysis built around rich image models combined with ensemble classifiers is a promising direction towards automatizing steganalysis for a wide spectrum of s…
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“Existing works (Fridrich and Kodovsky 2012) have proved that the high-frequency features can help to boost the generalization ability.”
From Dual Contrastive Learning for General Face Forgery Detection (2022) · cites this paper as Fridrich and Kodovsky 2012
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LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123
Information Hiding Techniques for Steganography and Digital Watermarking
Katzenbeisser, S., & Petitcolas, F. A. P. (1999) · Cited by 1,895
Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods
Kolda, T. G., Lewis, R. M., & Torczon, V. J. (2003) · SIAM Review · doi:10.1137/s003614450242889 · Cited by 1,659
Feature extraction : foundations and applications
Guyon, I., Gunn, S. R., Nikravesh, M., & zadeh, L. N. (2006) · Cited by 1,658
Ensemble Classifiers for Steganalysis of Digital Media
Kodovský, J., Fridrich, J., & Holub, V. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2175919 · Cited by 1,084
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 1,026
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 968
Self-citationSteganalysis by Subtractive Pixel Adjacency Matrix
Pevný, T., Bas, P., & Fridrich, J. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2045842 · Cited by 899
Self-citationSteganalysis by subtractive pixel adjacency matrix
Pevný, T., Bas, P., & Fridrich, J. (2009) · ACM workshop on Multimedia and security · doi:10.1145/1597817.1597831 · Cited by 771
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
Steganography in Digital Media
Fridrich, J. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9781139192903 · Cited by 568
Self-citationSteganalysis using image quality metrics
Avcıbaş, İ., Memon, N. D., & Sankur, B. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.807363 · Cited by 563
Steganography in digital media: principles, algorithms, and applications
(2010) · Choice Reviews Online · doi:10.5860/choice.47-6907 · Cited by 552
Fridrich, J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30114-1_6 · Cited by 465
Self-citationDetecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
Lyu, S., & Farid, H. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36415-3_22 · Cited by 456
Steganalysis of additive-noise modelable information hiding
Harmsen, J., & Pearlman, W. A. (2003) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.476813 · Cited by 400
A Markov Process Based Approach to Effective Attacking JPEG Steganography
Shi, Y. Q., Chen, C., & Chen, W. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74124-4_17 · Cited by 346
Gibbs Construction in Steganography
Filler, T., & Fridrich, J. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2077629 · Cited by 302
Self-citationNew blind steganalysis and its implications
Goljan, M., Fridrich, J., & Holotyak, T. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.643254 · Cited by 267
Self-citationJPEG image steganalysis utilizing both intrablock and interblock correlations
Chen, C., & Shi, Y. Q. (2008) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2008.4542096 · Cited by 244
Image Steganalysis with Binary Similarity Measures
Avcıbaş, İ., Kharrazi, M., Memon, N. D., & Sankur, B. (2005) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/asp.2005.2749 · Cited by 190
Revisiting weighted stego-image steganalysis
Ker, A. D., & Böhme, R. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.766820 · Cited by 181
Steganalysis in high dimensions: fusing classifiers built on random subspaces
Kodovský, J., & Fridrich, J. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.872279 · Cited by 128
Self-citationA machine learning based scheme for double JPEG compression detection
Chen, C., Shi, Y. Q., & Su, W. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761645 · Cited by 117
Steganalysis of Content-Adaptive Steganography in Spatial Domain
Fridrich, J., Kodovský, J., Holub, V., & Goljan, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24178-9_8 · Cited by 102
Self-citationDesign of adaptive steganographic schemes for digital images
Filler, T., & Fridrich, J. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.872192 · Cited by 97
Self-citationSteganalysis based on Markov Model of Thresholded Prediction-Error Image
Zou, D., Shi, Y. B., Su, W., & Xuan, G. (2006) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2006.262792 · Cited by 90
Modern steganalysis can detect YASS
Kodovský, J., Pevný, T., & Fridrich, J. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.838768 · Cited by 89
Self-citationBreaking HUGO – The Process Discovery
Fridrich, J., Kodovský, J., Holub, V., & Goljan, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24178-9_7 · Cited by 68
Self-citationSteganalysis of spread spectrum data hiding exploiting cover memory
Sullivan, K., Madhow, U., Chandrasekaran, S., & Manjunath, B. S. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.588121 · Cited by 66
A New Methodology in Steganalysis: Breaking Highly Undetectable Steganograpy (HUGO)
Gül, G., & Kurugöllü, F. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24178-9_6 · Cited by 66
Detection of ±1 LSB steganography based on the amplitude of histogram local extrema
Cancelli, G., Doërr, G., Cox, I. J., & Barni, M. (2008) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2008.4711998 · Cited by 61
On dangers of overtraining steganography to incomplete cover model
Kodovský, J., Fridrich, J., & Holub, V. (2011) · doi:10.1145/2037252.2037266 · Cited by 58
Self-citationOn completeness of feature spaces in blind steganalysis
Kodovský, J., & Fridrich, J. (2008) · ACM workshop on Multimedia and security · doi:10.1145/1411328.1411352 · Cited by 41
Self-citationFeature restoration and distortion metrics
Chonev, V., & Ker, A. D. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.872574 · Cited by 8
Flagged first, then by citations
1 of the 38 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- upham, 0, Steganographic Algorithm JSteg
- filler, 2010, BOSS (Break Our Steganography System)
- kirchner, 2010, On detection of median filtering in images, Proc SPIE Electronic Imaging Media Forensics and Security XII, 7542, 10 1
- kodovsk, 2011, On dangers of cross-validation in steganalysis Tech Rep
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Sorted by citations
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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 1,212
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Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00505 · Cited by 1,211
Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues
Qian, Y., Yin, G., Sheng, L., Chen, Z., & Shao, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58610-2_6 · Cited by 1,058
Deep Residual Network for Steganalysis of Digital Images
Boroumand, M., Chen, M., & Fridrich, J. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2871749 · Cited by 1,049
Self-citation
Metadata
- Authors
- Fridrich, J., & Kodovsky, J.
- Affiliations
- Binghamton University (US)
- ORCID
- Fridrich, Jessica
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2012-06
- Volume
- 7
- Issue
- 3
- Pages
- 868-882
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2009130368
- Cited by
- 2,144
- References
- 38
- Citations by year
- 2026: 169 · 2025: 198 · 2024: 222 · 2023: 218 · 2022: 235 · 2021: 234 · 2020: 206 · 2019: 173 · 2018: 127 · 2017: 100 · 2016: 86 · 2015: 74 · 2014: 63 · 2013: 28 · 2012: 11
- FWCI
- 57.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Funders
- Air Force Office of Scientific Research (FA9550-, FA9550-09-1-0147)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Keywords
- steganalysis, image steganography, ensemble classifiers, high-pass filter, Hugo
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