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…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
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
Self-citation
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
Cited by
Sorted by citations
Items 101–200 of 1,003
Rich model for Steganalysis of color images
Goljan, M., Fridrich, J., & Cogranne, R. (2014) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2014.7084325 · Cited by 156
Self-citationSetiadi, D. R. I. M., Rustad, S., Andono, P. N., & Shidik, G. F. (2022) · Signal Processing · doi:10.1016/j.sigpro.2022.108908 · Cited by 156
A review of image steganalysis techniques for digital forensics
Κarampidis, K., Kavallieratou, E., & Papadourakis, G. M. (2018) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2018.04.005 · Cited by 154
Data embedding in digital images using critical functions
Liao, X., Qin, Z., & Ding, L. (2017) · Signal Processing Image Communication · doi:10.1016/j.image.2017.07.006 · Cited by 150
Automatic Detection of Object-Based Forgery in Advanced Video
Chen, S., Tan, S., Li, B., & Huang, J. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2473436 · Cited by 149
A comprehensive overview of Deepfake: generation, detection, datasets, and opportunities
Seow, J. W., Lim, M. K., Phan, R. C. W., & Liu, J. K. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.135 · Cited by 149
IID-Net: Image Inpainting Detection Network via Neural Architecture Search and Attention
Wu, H., & Zhou, J. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3075039 · Cited by 155
The ALASKA Steganalysis Challenge
Cogranne, R., Giboulot, E., & Bas, P. (2019) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3335203.3335726 · Cited by 143
Ensemble of CNNs for Steganalysis
Xu, G., Wu, H., & Shi, Y. Q. (2016) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/2909827.2930798 · Cited by 140
Multivariate gaussian model for designing additive distortion for steganography
Fridrich, J., & Kodovský, J. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638198 · Cited by 138
Self-citationAltFreezing for More General Video Face Forgery Detection
Wang, Z., Bao, J., Zhou, W., Wang, W., & Li, H. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00402 · Cited by 137
CORE: Consistent Representation Learning for Face Forgery Detection
Ni, Y., Meng, D., Yu, C., Quan, C., Ren, D., & Zhao, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00011 · Cited by 136
A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
Marra, F., Gragnaniello, D., Verdoliva, L., & Poggi, G. (2020) · IEEE Access · doi:10.1109/access.2020.3009877 · Cited by 140
A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics
Camacho, I. C., & Wang, K. (2021) · Journal of Imaging · doi:10.3390/jimaging7040069 · Cited by 138
Use of a Capsule Network to Detect Fake Images and VideosPreprint
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12467 · Cited by 192
Ou, B., Li, X., & Wang, J. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.05.005 · Cited by 129
Secret-to-Image Reversible Transformation for Generative Steganography
Zhou, Z., Su, Y., Li, J., Yu, K., Wu, Q. M. J., Fu, Z., & Shi, Y.-Q. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3217661 · Cited by 132
Image manipulation detection by multiple tampering traces and edge artifact enhancement
Lin, X., Wang, S., Deng, J., Fu, Y., Bai, X. P., Chen, X., Qu, X., & Tang, W. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109026 · Cited by 128
Phase-aware projection model for steganalysis of JPEG images
Holub, V., & Fridrich, J. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2075239 · Cited by 125
Self-citationDistinguishing Between Natural and Computer-Generated Images Using Convolutional Neural Networks
Quan, W., Wang, K., Dong-ming Yan, A., & Zhang, X. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2834147 · Cited by 123
Mahmood, T., Mehmood, Z., Shah, M., & Saba, T. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.03.015 · Cited by 124
Detection Based Defense Against Adversarial Examples From the Steganalysis Point of View
Liu, J., Zhang, W., Zhang, Y., Hou, D., Liu, Y., Zha, H., & Yu, N. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00496 · Cited by 122
The Future of False Information Detection on Social Media: New Perspectives and Trends
Guo, B., Ding, Y., Yao, L., Liang, Y., & Yu, Z. (2021) · ACM Computing Surveys · doi:10.1145/3393880 · Cited by 122
ReST-Net: Diverse Activation Modules and Parallel Subnets-Based CNN for Spatial Image Steganalysis
Li, B., Wei, W., Ferreira, A., & Tan, S. (2018) · IEEE Signal Processing Letters · doi:10.1109/lsp.2018.2816569 · Cited by 126
Fast and Effective Global Covariance Pooling Network for Image Steganalysis
Deng, X., Chen, B., Luo, W., & Luo, D. (2019) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3335203.3335739 · Cited by 123
Single-image splicing localization through autoencoder-based anomaly detection
Cozzolino, D., & Verdoliva, L. (2016) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2016.7823921 · Cited by 122
Blurred Image Splicing Localization by Exposing Blur Type Inconsistency
Bahrami, K., Kot, A. C., Li, L., & Li, H. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2394231 · Cited by 121
Deep Learning for Deepfakes Creation and Detection: A Survey
Nguyen, T. T., Nguyen, M. C., Nguyen, D. T., Nguyen, D. T., & Nahavandi, S. (2020) · Figshare · doi:10.6084/m9.figshare.12731039.v1 · Cited by 122
Detect and locate: Exposing face manipulation by semantic-and noise-level telltales
Kong, C., Chen, B., Li, H., Wang, S., Rocha, A., & Kwong, S. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3169921 · Cited by 120
Coverless Image Steganography: A Survey
Qin, J., Luo, Y., Xiang, X., Tan, Y., & Huang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2955452 · Cited by 120
Content-adaptive pentary steganography using the multivariate generalized Gaussian cover model
Sedighi, V., Fridrich, J., & Cogranne, R. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2080272 · Cited by 118
Self-citationGBRAS-Net: A Convolutional Neural Network Architecture for Spatial Image Steganalysis
Tabares-Soto, R., Arteaga-Arteaga, H. B., Bravo-Ortíz, M. A., Mora-Rubio, A., Arias-Garzón, D., Alzate-Grisales, J. A., Jacome, A. B., Orozco-Arias, S., Isaza, G. A., & Ramos-Pollán, R. (2021) · IEEE Access · doi:10.1109/access.2021.3052494 · Cited by 133
Improving Steganographic Security by Synchronizing the Selection Channel
Denemark, T., & Fridrich, J. (2015) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/2756601.2756620 · Cited by 116
Self-citationAdaptive steganalysis against WOW embedding algorithm
Tang, W., Li, H., Luo, W., & Huang, J. (2014) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/2600918.2600935 · Cited by 115
Image Forgery Localization via Integrating Tampering Possibility Maps
Li, H., Luo, W., Qiu, X., & Huang, J. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2656823 · Cited by 115
Adversarial Examples Against Deep Neural Network based Steganalysis
Zhang, Y., Zhang, W., Chen, K., Liu, J., Liu, Y., & Yu, N. (2018) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3206004.3206012 · Cited by 114
Design Principles of Convolutional Neural Networks for Multimedia Forensics
Bayar, B., & Stamm, M. C. (2017) · Electronic Imaging · doi:10.2352/issn.2470-1173.2017.7.mwsf-328 · Cited by 113
Cozzolino, D., Gragnaniello, D., & Verdoliva, L. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026073 · Cited by 112
Image forgery detection through residual-based local descriptors and block-matching
Cozzolino, D., Gragnaniello, D., & Verdoliva, L. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026072 · Cited by 112
MARLIN: Masked Autoencoder for facial video Representation LearnINg
Cai, Z., Ghosh, S., Stefanov, K., Dhall, A., Cai, J., Rezatofighi, H., Haffari, R., & Hayat, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00150 · Cited by 122
Self-Adversarial Training Incorporating Forgery Attention for Image Forgery Localization
Zhuo, L., Tan, S., Li, B., & Huang, J. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3152362 · Cited by 118
Hierarchical Frequency-Assisted Interactive Networks for Face Manipulation Detection
Miao, C., Tan, Z., Chu, Q., Yu, N., & Guo, G. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3198275 · Cited by 117
Deep learning in steganography and steganalysis
Chaumont, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819438-6.00022-0 · Cited by 123
Deepfake Detection: A Comprehensive Survey from the Reliability Perspective
Wang, T., Liao, X., Chow, K. P., Lin, X., & Wang, Y. (2025) · ACM Computing Surveys · doi:10.1145/3699710 · Cited by 107
Deep Learning Local Descriptor for Image Splicing Detection and Localization
Rao, Y., Ni, J., & Zhao, H. (2020) · IEEE Access · doi:10.1109/access.2020.2970735 · Cited by 109
PRRNet: Pixel-Region relation network for face forgery detection
Shang, Z., Xie, H., Zha, Z.-J., Yu, L., Li, Y., & Zhang, Y. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107950 · Cited by 107
Image Forgery Localization based on Multi-Scale Convolutional Neural Networks
Liu, Y., Guan, Q., Zhao, X., & Cao, Y. (2018) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3206004.3206010 · Cited by 106
Edge-based image steganography
Islam, S., Modi, M. R., & Gupta, P. (2014) · EURASIP Journal on Information Security · doi:10.1186/1687-417x-2014-8 · Cited by 109
Designing Near-Optimal Steganographic Codes in Practice Based on Polar Codes
Li, W., Zhang, W., Li, L., Zhou, H., & Yu, N. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.2982624 · Cited by 105
Recent Advances of Image Steganography With Generative Adversarial Networks
Liu, J., Ke, Y., Zhang, Z., Yu, L., Li, J., Zhang, M., & Yang, X. (2020) · IEEE Access · doi:10.1109/access.2020.2983175 · Cited by 116
TransForensics: Image Forgery Localization with Dense Self-Attention
Hao, J., Zhang, Z., Yang, S., Xie, D., & Pu, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01478 · Cited by 106
CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis
Barni, M., Kallas, K., Nowroozi, E., & Tondi, B. (2020) · 2020 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs49906.2020.9360905 · Cited by 103
Steganography and Steganalysis (in digital forensics): a Cybersecurity guide
Dalal, M., & Juneja, M. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09929-9 · Cited by 101
Digital image steganography and steganalysis: A journey of the past three decades
Sahu, A. K., & Sahu, M. (2020) · Open Computer Science · doi:10.1515/comp-2020-0136 · Cited by 101
Decomposing Joint Distortion for Adaptive Steganography
Zhang, W., Zhang, Z. D., Zhang, L., Li, H., & Yu, N. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2587388 · Cited by 100
Deep Learning Applied to Steganalysis of Digital Images: A Systematic Review
Tabares-Soto, R., Ramos-Pollán, R., & Isaza, G. A. (2019) · IEEE Access · doi:10.1109/access.2019.2918086 · Cited by 100
A universal image forensic strategy based on steganalytic model
Qiu, X., Li, H., Luo, W., & Huang, J. (2014) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/2600918.2600941 · Cited by 97
Adaptive Steganalysis Based on Embedding Probabilities of Pixels
Tang, W., Li, H., Luo, W., & Huang, J. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2507159 · Cited by 97
Kheddar, H., Hemis, M., Himeur, Y., Megías, D., & Amira, A. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127528 · Cited by 123
Deep Learning for Deepfakes Creation and Detection.Preprint
Nguyen, T. T., Nguyen, C. M., Nguyen, D. T., Nguyen, T. T., & Nahavandi, S. (2019) · arXiv (Cornell University) · Cited by 97
DeepFake Face Image Detection based on Improved VGG Convolutional Neural Network
Chang, X., Wu, J., Yang, T., & Feng, G. (2020) · Chinese Control Conference (CCC) · doi:10.23919/ccc50068.2020.9189596 · Cited by 95
SSTNet: Detecting Manipulated Faces Through Spatial, Steganalysis and Temporal Features
Wu, X., Xie, Z., Gao, Y., & Xiao, Y. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9053969 · Cited by 94
Toward Construction-Based Data Hiding: From Secrets to Fingerprint Images
Li, S., & Zhang, X. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2878290 · Cited by 94
Ganguly, S., Ganguly, A., Mohiuddin, S., Malakar, S., & Sarkar, R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118423 · Cited by 101
Generative Steganography via Auto-Generation of Semantic Object Contours
Zhou, Z., Dong, X., Meng, R., Wang, M., Yan, H., Yu, K., & Choo, K. R. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3268843 · Cited by 91
Multiple Robustness Enhancements for Image Adaptive Steganography in Lossy Channels
Zhang, Y., Luo, X., Guo, Y., Qin, C., & Liu, F. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2923980 · Cited by 90
A Survey of Deep Learning-Based Source Image Forensics
Yang, P., Baracchi, D., Ni, R., Zhao, Y., Argenti, F., & Piva, A. (2020) · Journal of Imaging · doi:10.3390/jimaging6030009 · Cited by 117
Detecting Deepfakes with Metric Learning
Kumar, A., Bhavsar, A., & Verma, R. (2020) · 2020 8th International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf49977.2020.9107962 · Cited by 90
Wang, K., Zhao, H. V., & Wang, H. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2308633 · Cited by 89
Secure Robust JPEG Steganography Based on AutoEncoder With Adaptive BCH Encoding
Lu, W., Zhang, J., Zhao, X., Zhang, W., & Huang, J. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3027843 · Cited by 88
Learning Second Order Local Anomaly for General Face Forgery Detection
Fei, J., Dai, Y., Yu, P., Shen, T., Xia, Z., & Weng, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01963 · Cited by 88
Detecting fake images by identifying potential texture difference
Yang, J., Xiao, S., Li, A., Lan, G., & Wang, H. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.043 · Cited by 86
Detecting DeepFake, FaceSwap and Face2Face facial forgeries using frequency CNN
Kohli, A., & Gupta, A. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10420-8 · Cited by 90
Image Steganography With Symmetric Embedding Using Gaussian Markov Random Field Model
Su, W., Ni, J., Hu, X., & Fridrich, J. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3001122 · Cited by 85
Self-citationCamera model identification framework using an ensemble of demosaicing features
Chen, C., & Stamm, M. C. (2015) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2015.7368573 · Cited by 84
Deep Learning for Detection of Object-Based Forgery in Advanced Video
Yao, Y., Shi, Y.-Q., Weng, S., & Guan, B. (2017) · Symmetry · doi:10.3390/sym10010003 · Cited by 83
Li, B., Li, Z., Zhou, S., Tan, S., & Zhang, X. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2780805 · Cited by 82
WISERNet: Wider Separate-Then-Reunion Network for Steganalysis of Color Images
Zeng, J., Tan, S., Liu, G., Li, B., & Huang, J. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2904413 · Cited by 82
Is ensemble classifier needed for steganalysis in high-dimensional feature spaces?
Cogranne, R., Sedighi, V., Fridrich, J., & Pevný, T. (2015) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2015.7368597 · Cited by 79
Self-citationEvolving Generative Adversarial Networks to improve image steganography
Martín, A. G., Hernández, A., Alazab, M., Jung, J. J., & Camacho, D. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119841 · Cited by 78
Study of cover source mismatch in steganalysis and ways to mitigate its impact
Kodovský, J., Sedighi, V., & Fridrich, J. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2039693 · Cited by 77
Self-citationA feature-based approach for image tampering detection and localization
Verdoliva, L., Cozzolino, D., & Poggi, G. (2014) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2014.7084319 · Cited by 78
Unsupervised steganalysis based on artificial training sets
Lerch-Hostalot, D., & Megías, D. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2015.12.013 · Cited by 78
Fighting Fake News: Two Stream Network for Deepfake Detection via Learnable SRM
Han, B., Han, X., Zhang, H., Li, J., & Cao, X. (2021) · IEEE Transactions on Biometrics, Behavior, and Identity Science · doi:10.1109/tbiom.2021.3065735 · Cited by 77
ImageNet Pre-trained CNNs for JPEG Steganalysis
Yousfi, Y., Butora, J., Khvedchenya, E., & Fridrich, J. (2020) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs49906.2020.9360897 · Cited by 80
Self-citationLightweight and Effective Deep Image Steganalysis Network
Weng, S., Chen, M., Yu, L., & Sun, S. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3201727 · Cited by 77
CFL-Net: Image Forgery Localization Using Contrastive Learning
Niloy, F. F., Bhaumik, K. K., & Woo, S. S. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00462 · Cited by 78
A Contemporary Survey on Deepfake Detection: Datasets, Algorithms, and Challenges
Gong, L. Y., & Li, X. J. (2024) · Electronics · doi:10.3390/electronics13030585 · Cited by 75
LiSiam: Localization Invariance Siamese Network for Deepfake Detection
Wang, J., Sun, Y., & Tang, J. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3186803 · Cited by 73
Iris liveness detection for mobile devices based on local descriptors
Gragnaniello, D., Sansone, C., & Verdoliva, L. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.10.018 · Cited by 72
Detection of Fake Images Via The Ensemble of Deep Representations from Multi Color Spaces
He, P., Li, H., & Wang, H. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803740 · Cited by 72
Detection of Deepfake Videos Using Long-Distance Attention
Lu, W., Liu, L., Zhang, B., Luo, J., Zhao, X., Zhou, Y., & Huang, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3233063 · Cited by 78
Steganalysis of HUGO steganography based on parameter recognition of syndrome-trellis-codes
Luo, X., Song, X., Li, X., Zhang, W., Lu, J., Yang, C., & Liu, F. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2759-2 · Cited by 72
Multi-directional block based PVD and modulus function image steganography to avoid FOBP and IEP
Sahu, A. K., Swain, G., Sahu, M., & Hemalatha, J. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2021.102808 · Cited by 71
The Steganographer is the Outlier: Realistic Large-Scale Steganalysis
Ker, A. D., & Pevný, T. (2014) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2014.2336380 · Cited by 70
A Novel Convolutional Neural Network for Image Steganalysis With Shared Normalization
Wu, S., Zhong, S.-H., & Liu, Y. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2920605 · Cited by 72
Wavelet-packets for deepfake image analysis and detection
Wolter, M., Blanke, F., Heese, R., & Garcke, J. (2022) · Machine Learning · doi:10.1007/s10994-022-06225-5 · Cited by 84
Spatial Image Steganography Based on Generative Adversarial NetworkPreprint
Yang, J., Liu, K., Kang, X., Wong, E. K., & Shi, Y.-Q. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.07939 · Cited by 69
A deep learning approach for iris sensor model identification
Marra, F., Poggi, G., Sansone, C., & Verdoliva, L. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.04.010 · Cited by 68
Reversible Data Hiding in JPEG Images With Multi-Objective Optimization
Yin, Z., Ji, Y., & Luo, B. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2969463 · Cited by 68
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 ›
Are you an author, or do you see a problem with this page? Request a review
