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

Open this paper in CoChat — free account

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…

Integrity signals

Registry

Crossref · 10.1109/tifs.2012.2190402

Cites

37 of 37 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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
Bagging predictors
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-citation
Using 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-citation
Steganalysis 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

Show all 38 references

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

Sorted by citations

Items 601–700 of 1,003

Universal stego post-processing for enhancing image steganography
Chen, B., Luo, W., Zheng, P., & Huang, J. (2020) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2020.102664 · Cited by 19
A HEVC Video Steganalysis Against DCT/DST-Based Steganography
Shi, H., Sun, T., Jiang, X., Yi, D., & Xu, K. (2021) · International Journal of Digital Crime and Forensics · doi:10.4018/ijdcf.20210501.oa2 · Cited by 19
What makes the stego image undetectable?
Wu, S., Liu, Y., Zhong, S.-H., & Liu, Y. (2015) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2808492.2808539 · Cited by 19
Heard More Than Heard: An Audio Steganography Method Based on GANPreprint
Ye, D., Jiang, S., & Huang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.04986 · Cited by 19
Untitled work
(2020) · doi:10.1109/cvprw50498.2020.00329 · Cited by 18
MFCLIP: Multi-Modal Fine-Grained CLIP for Generalizable Diffusion Face Forgery Detection
Zhang, Y., Wang, T., Yu, Z., Gao, Z., Shen, L., & Chen, S. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3576577 · Cited by 27
Approaching Optimal Embedding In Audio Steganography With GAN
Yang, J., Zheng, H., Kang, X., & Shi, Y.-Q. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054397 · Cited by 18
Image Steganalysis via Diverse Filters and Squeeze-and-Excitation Convolutional Neural Network
Liu, F., Zhou, X. W., Yan, X., Lu, Y., & Wang, S. (2021) · Mathematics · doi:10.3390/math9020189 · Cited by 18
Capsule-Forensics Networks for Deepfake Detection
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2022) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-87664-7_13 · Cited by 21
Light-weight 3DCNN for DeepFakes, FaceSwap and Face2Face facial forgery detection
Kohli, A., & Gupta, A. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12778-3 · Cited by 18
Image steganalysis with multi-scale residual network
Chen, H., Han, Q., Li, Q., & Tong, X. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11611-7 · Cited by 18
Secure communication over wireless sensor network using image steganography with generative adversarial networks
Ambika, Virupakshappa, V., & Veerashetty, S. S. (2022) · Measurement Sensors · doi:10.1016/j.measen.2022.100452 · Cited by 18
SRTNet: a spatial and residual based two-stream neural network for deepfakes detection
Zhang, D., Zhu, W., Ding, X., Yang, G., Li, F., Deng, Z., & Song, Y. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13966-x · Cited by 20
Reinforcement learning of non-additive joint steganographic embedding costs with attention mechanism
Tang, W., Li, B., Li, W., Wang, Y., & Huang, J. (2023) · Science China Information Sciences · doi:10.1007/s11432-021-3453-5 · Cited by 18
A defensive framework for deepfake detection under adversarial settings using temporal and spatial features
Asha, S., Vinod, P., & Menon, V. G. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00695-x · Cited by 18
GP-Net: Image Manipulation Detection and Localization via Long-Range Modeling and Transformers
Peng, J., Liu, C., Pang, H., Gao, X., Cheng, G., & Hao, B. (2023) · Applied Sciences · doi:10.3390/app132112053 · Cited by 18
Fusion Transformer with Object Mask Guidance for Image Forgery Analysis
Karageorgiou, D., Kordopatis-Zilos, G., & Papadopoulos, S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00438 · Cited by 18
Pure spatial rich model features for digital image steganalysis
Wang, P., Wei, Z., & Xiao, L. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2521-9 · Cited by 18
Exploring Non-Additive Distortion in Steganography
Pevný, T., & Ker, A. D. (2018) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3206004.3206015 · Cited by 18
Detection of adaptive histogram equalization robust against JPEG compression
Barni, M., Nowroozi, E., & Tondi, B. (2018) · International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf.2018.8401564 · Cited by 18
RHFCN:: Fully CNN-based Steganalysis of MP3 with Rich High-pass Filtering
Wang, Y., Yi, X., Zhao, X., & Su, A. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683626 · Cited by 18
Feature-Preserving Tensor Voting Model for Mesh Steganalysis
Zhou, H., Chen, K., Zhang, W., Qin, C., & Yu, N. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2929041 · Cited by 18
Digital Cardan Grille
Liu, J., Zhou, T., Zhang, Z., Ke, Y., Yu, L., & Zhang, M. (2018) · doi:10.1145/3297156.3297255 · Cited by 18
A passive technique for detecting copy-move forgeries by image feature matching
Mahmood, T., Shah, M., Rashid, J., Saba, T., Nisar, M. W., & Asif, M. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09655-2 · Cited by 18
Multiperspective Progressive Structure Adaptation for JPEG Steganography Detection Across Domains
Jia, J., Luo, M., Liu, J., Ren, W., & Wang, L. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3054045 · Cited by 18
MixNet: A Robust Mixture of Convolutional Neural Networks as Feature Extractors to Detect Stego Images Created by Content-Adaptive Steganography
Amrutha, E., Arivazhagan, S., & Jebarani, W. S. L. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10661-0 · Cited by 18
Faster and transferable deep learning steganalysis on GPU
Ye, D., Shunzhi, J., Li, S., & ChangRui, L. (2019) · Journal of Real-Time Image Processing · doi:10.1007/s11554-019-00870-1 · Cited by 18
Deep Learning-based Quantitative Steganalysis to Detect Motion Vector Embedding of HEVC Videos
Huang, X., Hu, Y., Wang, Y., Liu, B., & Liu, S. (2020) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc50466.2020.00030 · Cited by 18
Methods and trends in detecting AI-generated images: A comprehensive review
Mahara, A., & Rishe, N. D. (2026) · Computer Science Review · doi:10.1016/j.cosrev.2026.100908 · Cited by 21
Steganalysis with CNN Using Multi-channels Filtered Residuals
Yuan, Y., Lu, W., Feng, B., & Weng, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68505-2_10 · Cited by 17
A robust JPEG compression detector for image forensics
Kumawat, C., & Pankajakshan, V. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.116008 · Cited by 17
Steganalysis using learned denoising kernels
Singh, B., Chhajed, M., Sur, A., & Mitra, P. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09960-w · Cited by 17
Detection and Localization of Video Transcoding From AVC to HEVC Based on Deep Representations of Decoded Frames and PU Maps
Yao, H., Ni, R., Amirpour, H., Timmerer, C., & Zhao, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3185890 · Cited by 17
Improving security for image steganography using content-adaptive adversarial perturbations
Luo, J., He, P., Liu, J., Wang, H., Wu, C., Yuan, C., & Xia, Q. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04321-6 · Cited by 17
A comprehensive review of video steganalysis
Bouzegza, M., Belatreche, A., Bouridane, A., & Tounsi, M. (2022) · IET Image Processing · doi:10.1049/ipr2.12573 · Cited by 17
ForensicNet : Modern convolutional neural network‐based image forgery detection network
Tyagi, S., & Yadav, D. (2023) · Journal of Forensic Sciences · doi:10.1111/1556-4029.15210 · Cited by 21
Image manipulation detection and localization using multi-scale contrastive learning
Bai, R. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111914 · Cited by 17
Multi-scale noise-guided progressive network for image splicing detection and localization
Zhang, D., Jiang, N., Li, F., Chen, J., Liao, X., Yang, G., & Ding, X. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124975 · Cited by 19
Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency
Zhang, D., He, R., Liao, X., Li, F., Chen, J., & Yang, G. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3455311 · Cited by 18
A Lightweight and Effective Image Tampering Localization Network With Vision Mamba
Guo, K., Cao, G., Lou, Z., Huang, X., & Liu, J. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3570240 · Cited by 19
Improved universal quantitative steganalysis in spatial domain using ELM ensemble
Chutani, S., & Goyal, A. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4656-3 · Cited by 17
Ensemble of CNN and rich model for steganalysis
Liu, K., Yang, J., & Kang, X. (2017) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip.2017.7965617 · Cited by 17
Splicing Detection for Combined DCT, DWT and Spatial Markov-Features Using Ensemble Classifier
Mehta, R., & Agarwal, N. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.05.143 · Cited by 17
Steganalysis of Content-Adaptive Steganography Based on Massive Datasets Pre-Classification and Feature Selection
Lu, J., Zhou, G., Yang, C., Li, Z., & Lan, M. (2019) · IEEE Access · doi:10.1109/access.2019.2896781 · Cited by 18
Image Steganography Based on Artificial Immune in Mobile Edge Computing With Internet of Things
Ding, X., Xie, Y., Li, P., Cui, M., & Chen, J. (2020) · IEEE Access · doi:10.1109/access.2020.3010513 · Cited by 17
MI-LFGOA: multi-island levy-flight based grasshopper optimization for spatial image steganalysis
Chhikara, S., & Kumar, R. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09328-0 · Cited by 17
En-VStegNET: Video Steganography using spatio-temporal feature enhancement with 3D-CNN and Hourglass
Jaiswal, A., Kumar, S., & Nigam, A. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206921 · Cited by 17
Distribution-Preserving-Based Automatic Data Augmentation for Deep Image Steganalysis
Zhang, J., Chen, K., Qin, C., Zhang, W., & Yu, N. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3119994 · Cited by 17
Demystifying Attention Mechanisms for Deepfake Detection
Das, A., Das, S., & Dantcheva, A. (2021) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg52635.2021.9667026 · Cited by 17
An Overview of Image Security Techiques
Madhu, B. R., Holi, G., & Murthy, S. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016911834 · Cited by 17
Steganographer detection via deep residual network
Zheng, M., Zhong, S.-H., Wu, S., & Jiang, J. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019320 · Cited by 17
Color noise correlation-based splicing detection for image forensics
Itier, V., Strauss, O., Morel, L., & Puech, W. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10326-5 · Cited by 18
Universal Image Manipulation Detection using Deep Siamese Convolutional Neural NetworkPreprint
Mazumdar, A., Singh, J., Tomar, Y. S., & Bora, P. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.06323 · Cited by 20
Siamese convolutional neural network‐based approach towards universal image forensics
Mazumdar, A., & Bora, P. K. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.1114 · Cited by 17
Forgery face detection via adaptive learning from multiple experts
Fu, X., Li, S., Yuan, Y., Li, B., & Li, X. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.017 · Cited by 17
The Circular U-Net with Attention Gate for Image Splicing Forgery Detection
Peng, J., Li, Y., Liu, C., & Gao, X. (2023) · Electronics · doi:10.3390/electronics12061451 · Cited by 16
Two-Stream Xception Structure Based on Feature Fusion for DeepFake Detection
Wang, B., Huang, L., Huang, T., & Ye, F. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-023-00312-8 · Cited by 17
Detecting Deepfake Videos using Spatiotemporal Trident Network
Lin, K., Han, W., Li, S., Gu, Z., Zhao, H., & Mei, Y. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3623639 · Cited by 19
An Image Steganoganalyzer With Comprehensive Detection Performance
He, J., Weng, S., Yu, L., Zhang, C., & Chen, W. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3332506 · Cited by 16
Joint Cost Learning and Payload Allocation With Image-Wise Attention for Batch Steganography
Tang, W., Zhou, Z., Li, B., Choo, K. R., & Huang, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3354411 · Cited by 17
Image Manipulation Detection With Cascade Hierarchical Graph Representation
Pan, W., Ma, W., Zhao, S., Gu, L., Shi, G., Xia, Z., & Wang, M. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3390127 · Cited by 17
Face Reconstruction-Based Generalized Deepfake Detection Model with Residual Outlook Attention
Shi, Z., Liu, W., & Chen, H. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3686162 · Cited by 17
A Novel Mapping Scheme for Steganalysis
Chen, L., Shi, Y. Q., Sutthiwan, P., & Niu, X. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40099-5_3 · Cited by 16
Improved steganalysis algorithm against motion vector based video steganography
Wu, H., Liu, Y., Huang, J., & Yang, X. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026115 · Cited by 16
CONTENT ADAPTIVE STEGANOGRAPHY - DESIGN AND DETECTION
Holub, V. (2014) · Cited by 16
Universal embedding strategy for batch adaptive steganography in both spatial and JPEG domain
Zhao, Z., Guan, Q., Zhao, X., Yu, H., & Liu, C. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5016-z · Cited by 16
How to Extract Image Features Based on Co-Occurrence Matrix Securely and Efficiently in Cloud Computing
Ren, Y., Zhang, X., Feng, G., Qian, Z., & Li, F. (2017) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2017.2737980 · Cited by 16
Steganographer Detection based on Multiclass Dilated Residual Networks
Zheng, M., Zhong, S.-H., Wu, S., & Jiang, J. (2018) · International Conference on Multimedia Retrieval · doi:10.1145/3206025.3206031 · Cited by 16
A posterior evaluation algorithm of steganalysis accuracy inspired by residual co-occurrence probability
Wang, L., Xu, Y., Zhai, L., Ren, Y., & Du, B. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.10.003 · Cited by 16
Deep learning for steganalysis based on filter diversity selection
Zhong, K., Feng, G., Shen, L., & Luo, J. T. (2018) · Science China Information Sciences · doi:10.1007/s11432-018-9640-7 · Cited by 16
Spatial Image Steganalysis Based on ResNeXt
Sharma, A., & Muttoo, S. K. (2018) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2018.8600132 · Cited by 16
Shortening the Cover for Fast JPEG Steganography
Li, W., Zhou, W., Zhang, W., Qin, C., Hu, H., & Yu, N. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2908689 · Cited by 16
Emerging applications of reversible data hiding
Hou, D., Zhang, W., Liu, J., Zhou, S., Chen, D., & Yu, N. (2019) · International Conference on Image and Graphics Processing (ICIGP) · doi:10.1145/3313950.3313952 · Cited by 16
Image steganography based on color palette transformation in color space
Margalikas, E., & Ramanauskaitė, S. (2019) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-019-0484-x · Cited by 16
Resampling parameter estimation via dual-filtering based convolutional neural network
Peng, L., Liao, X., & Chen, M. (2020) · Multimedia Systems · doi:10.1007/s00530-020-00697-y · Cited by 16
Universal steganalysis of spatial content-independent and content-adaptive steganographic algorithms using normalized feature derived from empirical mode decomposed components
Arivazhagan, S., Amrutha, E., & Jebarani, W. S. L. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116567 · Cited by 16
Double JPEG compression detection and localization based on convolutional auto-encoder for image content removal
Rahmati, M., Razzazi, F., & Behrad, A. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103429 · Cited by 16
Consensus-Clustering-Based Automatic Distribution Matching for Cross-Domain Image Steganalysis
Jia, J., Luo, M., Ma, S., Wang, L., & Liu, Y. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3155924 · Cited by 16
Hybrid deep-learning framework for object-based forgery detection in video
Tan, S., Chen, B., Zeng, J., Li, B., & Huang, J. (2022) · Signal Processing Image Communication · doi:10.1016/j.image.2022.116695 · Cited by 18
Steganography with High Reconstruction Robustness: Hiding of Encrypted Secret Images
Zhu, X., Lai, Z., Zhou, N., & Wu, J. (2022) · Mathematics · doi:10.3390/math10162934 · Cited by 16
GAN-Based Steganography with the Concatenation of Multiple Feature Maps
Wu, H., Li, F., Zhang, X., & Wu, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-43575-2_1 · Cited by 16
Spec-ResNet: A General Audio Steganalysis scheme based on Deep Residual Network of SpectrogramPreprint
Ren, Y., Liu, D., Xiong, Q., Fu, J., & Wang, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.06838 · Cited by 16
MH-FFNet: Leveraging mid-high frequency information for robust fine-grained face forgery detection
Zhou, K., Sun, G., Wang, J., Yu, L., & Li, T. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127108 · Cited by 15
Theoretical model of the FLD ensemble classifier based on hypothesis testing theory
Cogranne, R., Denemark, T., & Fridrich, J. (2014) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2014.7084322 · Cited by 15
Self-citation
A Generalizable Deepfake Detector based on Neural Conditional Distribution Modelling
Khodabakhsh, A., & Busch, C. (2020) · NORA - Norwegian Open Research Archives · Cited by 15
Hybrid convolutional neural network architecture driven by residual features for steganalysis of spatial steganographic algorithms
Arivazhagan, S., Amrutha, E., Jebarani, W. S. L., & Veena, S. T. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05837-7 · Cited by 15
A Quaternion Two‐Stream R‐CNN Network for Pixel‐Level Color Image Splicing Localization
Chen, B., Ju, X., Gao, Y., & Wang, J. (2021) · Chinese Journal of Electronics · doi:10.1049/cje.2021.08.004 · Cited by 15
BZNet: Unsupervised Multi-scale Branch Zooming Network for Detecting Low-quality Deepfake Videos
Lee, S., An, J., & Woo, S. S. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512245 · Cited by 16
SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection
Zhao, K., Yuan, X., Xie, Z., Xiang, Y., Huang, G., & Feng, L. (2023) · Sensors · doi:10.3390/s23146430 · Cited by 15
Advanced Image Steganography Using a U-Net-Based Architecture with Multi-Scale Fusion and Perceptual Loss
Zeng, L., Yang, N., Li, X., Chen, A., Jing, H., & Zhang, J. (2023) · Electronics · doi:10.3390/electronics12183808 · Cited by 15
COMICS: End-to-End Bi-Grained Contrastive Learning for Multi-Face Forgery Detection
Zhang, C., Qi, H., Wang, S., Li, Y., & Lyu, S. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3405563 · Cited by 19
A two-stream-network based steganalysis network: TSNet
Guo, F., Sun, S., Weng, S., Yu, L., & He, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124796 · Cited by 15
Local Region Frequency Guided Dynamic Inconsistency Network for Deepfake Video Detection
Yue, P., Chen, B., & Fu, Z. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020030 · Cited by 15
Image Steganalysis Based on Dual-Path Enhancement and Fractal Downsampling
Fu, T., Chen, L., Jiang, Y., Jia, J., & Fu, Z. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3493615 · Cited by 16
Color Image High-Capacity Differential Steganography Algorithm Based on Multiple Adversarial Networks
Ma, B., Wang, H., Xu, J., Wang, X., Li, X., & Li, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3508849 · Cited by 15
Mutual Information-Optimized Steganalysis for Generative Steganography
Hu, M., & Wang, H. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3539089 · Cited by 17
Effect of Cover Quantization on Steganographic Fisher Information
Fridrich, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2235832 · Cited by 15
Self-citation
Source Camera Model Identification Using Features from Contaminated Sensor Noise
Tuama, A., Comby, F., & Chaumont, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31960-5_8 · Cited by 15
Bio-Inspired Computational Algorithms for Improved Image Steganalysis
Christaline, J. A., Ramesh, R., & Vaishali, D. (2016) · Indian Journal of Science and Technology · doi:10.17485/ijst/2016/v9i10/88995 · Cited by 15
Embedding Strategy for Batch Adaptive Steganography
Zhao, Z., Guan, Q., Zhao, X., Yu, H., & Liu, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-53465-7_37 · Cited by 15

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

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

Rich Models for Steganalysis of Digital Images

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks