Cited by 2,144Advanced Steganography and Watermarking TechniquesDigital Media Forensic DetectionImage and Video Quality Assessment

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

Fridrich, Jessica, Kodovsky, Jan·2012·IEEE Transactions on Information Forensics and Security

DOI 10.1109/tifs.2012.2190402

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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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Authors: Fridrich, J., & Kodovsky, J.
Affiliations: Binghamton University (US)
ORCID: Fridrich, Jessica
Venue: IEEE Transactions on Information Forensics and Security
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2012-06
Volume: 7
Issue: 3
Pages: 868-882
ISSN: 1556-6013 (ISSN-L)
1556-6021
DOI: 10.1109/tifs.2012.2190402
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
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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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Rich Models for Steganalysis of Digital Images

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