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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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Cites
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
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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
Cited by
Sorted by citations
Items 701–800 of 1,003
An embedding mechanism for natural steganography after down-sampling
Bas, P. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952532 · Cited by 15
An AMR adaptive steganographic scheme based on the pitch delay of unvoiced speech
Ren, Y., Liu, D., Yang, J., & Wang, L. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6600-6 · Cited by 15
A Simple and Effective Initialization of CNN for Forensics of Image Processing Operations
Camacho, I. C., & Wang, K. (2019) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3335203.3335723 · Cited by 15
Xception-Based General Forensic Method on Small-Size Images
Yang, L., Yang, P., Ni, R., & Zhao, Y. (2019) · Smart innovation, systems and technologies · doi:10.1007/978-981-13-9710-3_38 · Cited by 15
An embedding strategy on fusing multiple image features for data hiding in multiple images
Yang, J., & Liao, X. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.102822 · Cited by 15
Comprehensive Criteria-Based Generalized Steganalysis Feature Selection Method
Wang, Y., Ma, Y., Jin, R., Liu, P., & Ruan, N. (2020) · IEEE Access · doi:10.1109/access.2020.3018709 · Cited by 15
Repeatable Data Hiding: Towards the Reusability of Digital Images
Wang, Z., Feng, G., & Zhang, X. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3057599 · Cited by 15
A New Cost Function for Spatial Image Steganography Based on 2D-SSA and WMF
Xie, G., Ren, J., Marshall, S., Zhao, H., & Li, H. (2021) · IEEE Access · doi:10.1109/access.2021.3059690 · Cited by 15
Steganalysis of convolutional neural network based on neural architecture search
Wang, H., Pan, X., Fan, L., & Zhao, S. (2021) · Multimedia Systems · doi:10.1007/s00530-021-00779-5 · Cited by 15
Benrhouma, O. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13350-9 · Cited by 15
IR-Capsule: Two-Stream Network for Face Forgery Detection
Lin, K., Han, W., Li, S., Gu, Z., Zhao, H., Ren, J., Zhu, L., & Lv, J. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10008-4 · Cited by 16
Li, F., Pei, Z., Wei, W., Li, J., & Qin, C. (2022) · Security and Communication Networks · doi:10.1155/2022/1090307 · Cited by 17
Reversible visual transformation via exploring the correlations within color images
Hou, D., Qin, C., Yu, N., & Zhang, W. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.11.014 · Cited by 15
A Convolutional Neural Network Based Seam Carving Detection Scheme for Uncompressed Digital Images
Ye, J., Shi, Y., Xu, G., & Shi, Y.-Q. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11389-6_1 · Cited by 15
Anti-forensic Approach to Remove StegoContent from Images and Videos
Amritha, P. P., Sethumadhavan, M., Krishnan, R., Pal, S. K., & Scientific Analysis Group, D. (2019) · Journal of Cyber Security and Mobility · doi:10.13052/jcsm2245-1439.831 · Cited by 15
A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model
Qin, X., Li, B., & Huang, J. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682688 · Cited by 15
Direct Adversarial Attack on Stego Sandwiched Between Black Boxes
Zha, H., Zhang, W., Qin, C., & Yu, N. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8804415 · Cited by 15
MP3 steganalysis based on joint point-wise and block-wise correlations
Wang, Y., Yi, X., & Zhao, X. (2019) · Information Sciences · doi:10.1016/j.ins.2019.10.037 · Cited by 15
Alhaddad, M. J., Alkinani, M. H., Atoum, M. S., & Alarood, A. A. (2020) · Symmetry · doi:10.3390/sym12122071 · Cited by 15
Multi-definition Deepfake detection via semantics reduction and cross-domain training
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Deep residual network for halftone image steganalysis with stego-signal diffusion
Zeng, L., Lu, W., Liu, W., & Chen, J. (2020) · Signal Processing · doi:10.1016/j.sigpro.2020.107576 · Cited by 15
Three-classification face manipulation detection using attention-based feature decomposition
Cao, Y., Chen, J., Huang, L., Huang, T., & Ye, F. (2022) · Computers & Security · doi:10.1016/j.cose.2022.103024 · Cited by 14
Localization of Inpainting Forgery With Feature Enhancement Network
Zhang, Y., Fu, Z., Qi, S., Xue, M., Hua, Z., & Xiang, Y. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3225194 · Cited by 15
A Siamese Inverted Residuals Network Image Steganalysis Scheme based on Deep Learning
Li, H., Wang, J., Xiong, N. N., Zhang, Y., Vasilakos, A. V., & Luo, X. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3579166 · Cited by 14
Retracted: Steganalysis using Convolutional Neural Networks-Yedroudj Net
Vijjapu, A., Vinod, Y. S., Murty, S. V. S. N., Raju, B. E., Satyanarayana, B. V. V., & Kumar, G. P. (2023) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci56745.2023.10128190 · Cited by 14
RetractedGan, Y., Yang, J., & Zhong, J. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/8378073 · Cited by 14
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Hu, M., & Wang, H. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3300792 · Cited by 14
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Weng, S., Sun, S., & Yu, L. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3336561 · Cited by 14
CNN based image resizing forensics for double compressed JPEG images
Billa, N. R., Das, B. P., Biswal, M., & Okade, M. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103693 · Cited by 14
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Su, Z., Zhang, G., Shi, Z., Hu, D., & Zhang, W. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3372139 · Cited by 14
Ye, P., Yu, Q., Fu, G., Zhang, W., & Duan, C. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103743 · Cited by 14
Hameed, M. A., Hassaballah, M., & Qiao, T. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01332-w · Cited by 14
Steganalysis Network With Two-Branch Preprocessing for Spatial and JPEG Domains
He, J., Weng, S., Yu, L., & Chen, D. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3470809 · Cited by 14
Defining cost functions for adaptive steganography at the microscale
Chen, K., Zhang, W., Zhou, H., Yu, N., & Feng, G. (2016) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2016.7823900 · Cited by 14
Smell Bees Optimization for new embedding steganographic scheme in spatial domain
Ali, Y. M. B. (2018) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2018.08.003 · Cited by 14
Resampling detection of recompressed images via dual-stream convolutional neural network
Cao, G., Zhou, A., Huang, X., Song, G., Yang, L., & Zhu, Y. (2019) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2019253 · Cited by 14
Anti-forensics of median filtering and contrast enhancement
Sharma, S., Ravi, H., Subramanyam, A. V., & Emmanuel, S. (2020) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.102682 · Cited by 14
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Li, L., Zhang, W., Chen, K., & Yu, N. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2959909 · Cited by 14
Adversarial Attack on Deep Learning-Based Splice Localization
Rozsa, A., Boult, T. E., & Zhong, Z. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00332 · Cited by 14
Color image steganalysis based on embedding change probabilities in differential channels
Yang, C., Kang, Y., Liu, F., Song, X., Wang, J., & Luo, X. (2020) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147720917826 · Cited by 14
Face Anti-Spoofing by Fusing High and Low Frequency Features for Advanced Generalization Capability
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Sultan, B., & ArifWani, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14348-7 · Cited by 14
Joint adjustment image steganography networks
Zhang, L., Lu, Y., Li, T., & Lu, G. (2023) · Signal Processing Image Communication · doi:10.1016/j.image.2023.117022 · Cited by 14
A sequential method for online steganalysis
Cogranne, R. (2015) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2015.7368596 · Cited by 14
Combined and Calibrated Features for Steganalysis of Motion Vector-Based Steganography in H.264/AVC
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Mehrish, A., Subramanyam, A. V., & Emmanuel, S. (2019) · IEEE Access · doi:10.1109/access.2019.2901345 · Cited by 14
Jin, Z., Yu, Y., Chen, Y., & Chen, Y. (2020) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147720911002 · Cited by 14
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Shi, X., Li, P., Wu, H., Chen, Q., & Zhu, H. (2023) · IET Image Processing · doi:10.1049/ipr2.12763 · Cited by 13
Rana, K., Singh, G., & Goyal, P. (2023) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.05.019 · Cited by 13
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Ma, Y., Yang, Z., Li, T., Xu, L., & Qiao, Y. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.06.034 · Cited by 13
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Kamal, A. H. M., & Islam, M. M. (2015) · IEEE International Conference on Advanced Networks and Telecommunications Systems · doi:10.1109/ants.2015.7413636 · Cited by 13
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Fan, L., Sun, W., & Feng, G. (2018) · Mobile Networks and Applications · doi:10.1007/s11036-018-1167-z · Cited by 13
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Review on effectiveness of deep learning approach in digital forensics
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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