article

Deep residual learning for image steganalysis

Authors:

Shenghua Zhong,

Yan LiuAuthors Info & Claims

Multimedia Tools and Applications, Volume 77, Issue 9

Pages 10437 - 10453

https://doi.org/10.1007/s11042-017-4440-4

Published: 01 May 2018 Publication History

Abstract

Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional steganalytic methods. Following this idea, we propose a novel CNN model for image steganalysis based on residual learning. The proposed Deep Residual learning based Network (DRN) shows two attractive properties than existing CNN based methods. First, the model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. Second, the residual learning in DRN preserves the stego signal coming from secret messages, which is extremely beneficial for the discrimination of cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model can detect the state of arts steganographic algorithms at a high accuracy. It also outperforms the classical rich model method and several recently proposed CNN based methods.

References

[1]

Bas P, Filler T, Pevny T (2011) Break our steganographic system: The ins and outs of organizing BOSS. Information Hiding pp 59---70

Digital Library

[2]

Bianchini M, Scarselli F (2014) On the complexity of neural network classifiers: a comparison between shallow and deep architectures. IEEE Trans Neural Netw Learn Syst 25(8):1553---1565

[3]

Cheddad A, Condell J, Curran K, Kevitt PM (2010) Digital image steganography: Survey and analysis of current methods. Signal Process 90(3):727---752

Digital Library

[4]

Chen H, Ni D, Qin J, Li S, Yang X, Wang T, Heng PA (2015) Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE J Biomed Health Inf 19(5):1627--- 1636

[5]

Couchot JF, Couturier R, Guyeux C, Salomon M (2016) Steganalysis via a convolutional neural network using large convolution filters for embedding process with same stego key. arXiv:1605.07946v3

[6]

Denemark T, Sedighi V, Holub V, Cogranne R, Fridrich J (2014) Selection-channel-aware rich model for steganalysis of digital images. IEEE Workshop on Information Forensic and Security (WIFS)

[7]

Fridrich J, Goljan M (2002) Practical steganalysis of digital images - state of the art. Proc SPIE Photonics Imaging, Secur Watermarking Multimed Contents 4675:1---13

[8]

Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensic Secur 7(3):868---882

Digital Library

[9]

Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS)

[10]

He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv:1512.03385v1

[11]

He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. arXiv:1502.01852v1

[12]

Holub V, Fridrich J (2013) Random projections of residuals for digital image steganalysis. IEEE Trans Inf Forensic Secur 8(12):1996---2006

Digital Library

[13]

Holub V, Fridrich J, Denemark T (2014) Universal distortion function for steganography in an arbitrary domain. EURASIP J Inf Secur 1(1):1---13

[14]

Holub V, Fridrich J (2012) Designing steganographic distortion using directional filters. IEEE Workshop on Information Forensic and Security (WIFS)

[15]

Kodovsky J, Fridrich J, Holub V (2012) Ensemble classifiers for steganalysis of digital media. IEEE Trans Inf Forensic Secur 7(2):432---444

Digital Library

[16]

Li B, Huang J, Shi YQ (2009) Steganalysis of YASS. IEEE Trans Inf Forensic Secur 4(3):369---382

Digital Library

[17]

Li B, Wang M, Li X, Tan S, Huang J (2015) A strategy of clustering modification directions in spatial image steganography. IEEE Trans Inf Forensic Secur 10(9):1905---1917

[18]

Li B, He J, Huang J, Shi YQ (2011) A survey on image steganography and steganalysis. J Inf Hiding Multimed Signal Process 2(2):142---172

[19]

Li B, Wang M, Huang J, Li X (2014) A new cost function for spatial image steganography. IEEE International Conference on Image Processing (ICIP) pp 4206---4210

[20]

Lu H et al (2016) Wound intensity correction and segmentation with convolutional neural networks. Concurrency and Computation: Practice and Experience

[21]

Lyu S, Farid H (2004) Steganalysis using color wavelet statistics and one-class support vector machines. SPIE Symposium on Electronic Imaging pp 35---45

[22]

Li Y et al (2016) Underwater image de-scattering and classification by deep neural network. Comput Electr Eng 54:68---77

Digital Library

[23]

Pibre L, Pasquet J, Ienco D, Chaumont M (2016) Deep learning for steganalysis is better than a rich model with an ensemble classifier and is natively robust to the cover source-mismatch. SPIE Media Watermarking, Security, and Forensics

[24]

Pevny T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensic Secur 5(2):215---224

Digital Library

[25]

Provos N, Honeyman P (2002) Detecting steganographic content on the internet. Proceedings of Network and Distributed System Security Symposium (NDSS)

[26]

Qian Y, Dong J, Wang W, Tan T (2015) Deep learning for steganalysis via convolutional neural networks. SPIE Media Watermarking, Security, and Forensics, vol 9409

[27]

Ren T, Liu Y, Ju R, Wu G (2016) How important is location information in saliency detection of natural images. Multimed Tools Appl 75(5):2543---2564

Digital Library

[28]

Ren T, Qiu Z, Liu Y, Yu T, Bei J (2015) Soft-assigned bag of features for object tracking. Multimed Syst J 21(2):189---205

Digital Library

[29]

Sedighi V, Cogranne R, Fridrich J (2016) Content-Adaptive steganography by minimizing statistical detectability. IEEE Trans Inf Forensic Secur 1(2):221---234

[30]

Simonyan K, Zisserman A (2014) Very deep convolutional networks for large scale image recognition. arXiv:1409.1556v6

[31]

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition

[32]

Tan S, Li B (2014) Stacked convolutional auto-encoders for steganalysis of digital images. Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA) pp 1---4

[33]

Wang H, Wang S (2004) Cyber warfare: steganography vs. steganalysis. Commun ACM 47(10):76---82

Digital Library

[34]

Wu H-T, Huang J, Shi YQ (2015) A reversible data hiding method with contrast enhancement for medical images. J Vis Commun Image Represent 31:146---153

Digital Library

[35]

Wu J, Zhong SH, Jiang J, Yang Y (2016) A novel clustering method for static video summarization. Multimed Tools Appl 2016:1---17

Digital Library

[36]

Xu G, Wu H, Shi YQ (2016a) Structural design of convolutional neural networks for steganalysis. IEEE Signal Process Lett 23(5):708---712

[37]

Xu G, Wu H, Shi YQ (2016b) Ensemble of CNNs for steganalysis: an empirical study. Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security pp 103---107

Digital Library

[38]

Xu X, He L, Lu H, Shimada A, Taniguchi R (2016) Non-linear matrix completion for social image tagging. IEEE Access 2016:1---7

[39]

Zhong SH, Liu Y, Hua K (2016) Field effect deep networks for image recognition with incomplete data. ACM Trans Multimed Comput Commun Appl 12(Article):52

Digital Library

[40]

Zhong SH, Liu Y, Li B, Long J (2015) Query-oriented unsupervised multi-document summarization via deep learning. Expert Syst Appl 42(21):8146---8155

Digital Library

Cited By

Kombrink MGeradts ZWorring M(2024)Image Steganography Approaches and Their Detection Strategies: A SurveyACM Computing Surveys10.1145/369496557:2(1-40)Online publication date: 10-Oct-2024
https://dl.acm.org/doi/10.1145/3694965
Mao CLi ZLuo X(2024)A Covert Communication Method Based on Time Attributes Shifting of Online Video Bullet CommentIEEE Transactions on Consumer Electronics10.1109/TCE.2024.343969170:4(6472-6483)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TCE.2024.3439691
Kheddar HHemis MHimeur YMegías DAmira A(2024)Deep learning for steganalysis of diverse data typesNeurocomputing10.1016/j.neucom.2024.127528581:COnline publication date: 7-May-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127528
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Deep residual learning for image steganalysis
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cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 77, Issue 9

May 2018

1166 pages

ISSN:1380-7501

Issue’s Table of Contents

Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 May 2018

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Cited By

Kombrink MGeradts ZWorring M(2024)Image Steganography Approaches and Their Detection Strategies: A SurveyACM Computing Surveys10.1145/369496557:2(1-40)Online publication date: 10-Oct-2024
https://dl.acm.org/doi/10.1145/3694965
Mao CLi ZLuo X(2024)A Covert Communication Method Based on Time Attributes Shifting of Online Video Bullet CommentIEEE Transactions on Consumer Electronics10.1109/TCE.2024.343969170:4(6472-6483)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TCE.2024.3439691
Kheddar HHemis MHimeur YMegías DAmira A(2024)Deep learning for steganalysis of diverse data typesNeurocomputing10.1016/j.neucom.2024.127528581:COnline publication date: 7-May-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127528
Agarwal SJung K(2024)Digital image steganalysis using entropy driven deep neural networkJournal of Information Security and Applications10.1016/j.jisa.2024.10379984:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.jisa.2024.103799
Peng YYu QFu GZhang WDuan C(2024)Improving the robustness of steganalysis in the adversarial environment with Generative Adversarial NetworkJournal of Information Security and Applications10.1016/j.jisa.2024.10374382:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.jisa.2024.103743
Mohammed SMurugan B(2024)A novel image denoising algorithm based on least square generative adversarial networkJournal of Real-Time Image Processing10.1007/s11554-024-01447-321:3Online publication date: 24-Apr-2024
https://dl.acm.org/doi/10.1007/s11554-024-01447-3
Peng YFu GYu QLuo YHu JDuan C(2024)Enhancing the anti-steganalysis ability of steganography via adversarial examplesMultimedia Tools and Applications10.1007/s11042-023-15306-z83:2(6227-6247)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15306-z
Wang ZWang RWang MLai TZhang M(2024)Self-supervised Transformer-Based Pre-training Method with General Plant Infection DatasetPattern Recognition and Computer Vision10.1007/978-981-97-8490-5_14(189-202)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1007/978-981-97-8490-5_14
Lian WZhang YChen XJia BZhang X(2023)IPCADP-EqualizerInternational Journal of Intelligent Systems10.1155/2023/63577502023Online publication date: 1-Jan-2023
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Zhou WZhang HYan WLin W(2023)MMSMCNet: Modal Memory Sharing and Morphological Complementary Networks for RGB-T Urban Scene Semantic SegmentationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.327531433:12(7096-7108)Online publication date: 1-Dec-2023
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