Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 325-331.doi: 10.11896/JsJkx.190600103

• Information Security • Previous Articles     Next Articles

Overview of Research on Image Steganalysis Algorithms

PENG Wei1, HU Ning2 and HU Jing-Jing1   

  1. 1 College of Computer,National University of Defense Technology,Changsha 410073,China
    2 Institute of Cyber-space Security,Guangzhou University,Guangzhou 510006,China
  • Published:2020-07-07
  • About author:PENG Wei, research fellow at department of cyber security, college of computer, national university of defense technology, is a senior member of CCF.His main research interests include techniques of computer networks and network security.

Abstract: Image steganography is the technique to hide sensitive or secret data in digital pictures transmitted on the Internet.It has gone through fast development during the past two decades,and is utilized by criminals including terrorists to exchange information which may threaten social security.Many kinds of image steganalysis techniques have been developed to fight back the threat.By examining the secret information hidden in the suspicious images,image steganalysis can provide digital forensic evidence.This paper firstly gave a survey on the research status of algorithms of image steganography,then introduced and summarized the image steganalysis techniques by classifying them into two categories:specialized algorithms and generalized algorithms.For specialized algorithms,the approaches designed for specific image steganography algorithms and specific image types are introduced respectively.For generalized algorithms,the general procedures of image steganalysis based on image features are described.Then several classes of image features used for image steganalysis are summarized.Furthermore,the techniques used in general image steganalysis including machine learning-based classification and feature selection are analyzed by reviewing the existing research work on image stenanalysis.At last,a brief discussion on future research directions of image steganalysis is presented.

Key words: Feature selection, Image steganalysis, Image steganalysis algorithm, Information hiding, Machine learning

CLC Number: 

  • TP309.2
[1] LIU H X,XIA C H.Overview of Steganalytic Algorithm to Digi-tal Images .Computer Engineering and Design,2006,27(1):21-25.
[2] WANG S Z,ZHANG X P,ZHANG W M.Recent Advances in Image Based Steganalysis Research .Chinese Journal of Computers,2009,32(7):1247-1263.
[3] ZHANG J,XIONG F,ZHANG D.Overview on Image Steganaly-sis Technology .Computer Engineering,2013,39(4):165-168.
[4] DONG J,QIAN Y L,WANG W.Recent Advances in Image Steganalysis .Journal of Image and Signal Processing,2017,6(3):131-138.
[5] KARAMPIDIS K,KAVALLIERATOU E,PAPADOURAKIS G.A Review of Image Steganalysis Techniques for Digital Forensics .Journal of Information Security and Applications,2018,40:217-235.
[6] GUAN Q X,ZHU J,ZHAO X F,et al.Image Steganalysis Based on Linear Programing Feature Selection and Ensemble Classifier .Journal of Cyber Security,2018,3(1):83-94.
[7] KADHIM I J,PREMARATNE P,VIAL P J,et al.Comprehensive Survey of Image Steganography:Techniques,Evaluations,and Trends in Future Research .Neurocomputing,2019,335:299-326.
[8] PROVES N,HONCYMAN P.Hide and Seek:An Introduction to Steganography .IEEE Security & Privacy,2003,1(3):32-44.
[9] PAN F,LI J,YANG X.Image Steganography Method based on PVD and Modulus Function // Proceedings of the 2011 International Conference on Electronics,Communications and Control (ICECC),2011:282-284.
[10] KAWAGUCHI K,EASON R O.Principle and Application of BPCS Steganography // Proceedings of SPIE Multimedia Systems and Applications.Boston,1998:464-472.
[11] FRIDRICH J,GOLJAN M,DU R.Detecting LSB Steganography in Color and Gray-Scale Images .IEEE Multimedia,2001,8(4):22-28.
[12] Proves N.Defending Against Statistical Steganalysis // 10th USENIX Security Symposium.2001:24-25.
[13] WESTFELD A.F5-A Steganographic Algorithm:High Capacity Despite Better Steganalysis//Proceedings of 4th InternationalInformation Hiding Workshop.Pittsburgh,2001:289-302.
[14] KUMAR V,KUMAR D.A Modified DWT-based Image Steganography Technique .Multimedia Tools and Applications,2018,77(11):13279-13308.
[15] SALLEE P.Model-based Steganography // Proceedings of the International Workshop on Digital Watermarking.LNCS,vol.2939,Springer,2003:154-167.
[16] SHAFEE S,RAJAEI B.A Secure Steganography Algorithm Using Compressive Sensing based on HVS Feature // Proceedings of the 2017 Seventh International Conference on Emerging Security Technology.IEEE,2017:74-78.
[17] GIRDHAR A,KUMAR V.Comprehensive Survey of 3D Image Steganography Techniques .IET Image Processing,2018,12(1):1-10.
[18] RABIE T,KAMEL I.High-capacity Steganography:a Global-adaptive-region Discrete Cosine Transform Approach .Multimedia Tools and Applications,2017,76(5):6473-6493.
[19] HONG W.Human Visual System based Data Embedding Method Using Quadtree Partitioning .Signal Processing:Image Communication,2012,27(10):1123-1133.
[20] XIAO J J,LU Q.Adaptive Steganography Algorithm Based on Vision Effect .Journal of Test and Measurement Technology,2012,26(1):9-14.
[21] ZHOU Z,SUN H,HARIT R,et al.Coverless Image Steganography without Embedding // International Conference on Cloud Computing and Security,LNCS,vol.9483.Springer International Publishing,2015:123-132.
[22] WU J,LIU Y,DAI Z,et al.A Coverless Information Hiding Algorithm Based on Grayscale Gradient Co-occurrence Matrix .IETE Technical Review,2018,35(sup1):23-33.
[23] RUAN S H,QIN Z C.Coverless Covert Communication based on GIF Image .Communications Technology,2017,50(7):160-167.
[24] ZHANG X,PENG F,LONG M.Robust Coverless Image Steganography Based on DCT and LDA Topic Classification .IEEE Transactions on Multimedia,2018,20(12):3223-3238.
[25] DUAN X,SONG H,QIN C,et al.Coverless Steganography for Digital Images Based on a Generative Model .Computers,Materials & Continua,2018,55(3):483-493.
[26] WESTFELD A,PFITZMANN A.Attacks on Steganographic Systems // Proc.of International Workshop on Information Hiding (IH’99).Springer-Verlag,LNCS,1999:61-76.
[27] KWANGSOO L,JUNG C,LEE S,et al.New Steganalysis Methodology:LR Cube Analysis for the Detection of LSB Steganography // Proc.of International Workshop on Information Hiding (IH’05).Springer-Verlag,LNCS,2005:312-326.
[28] ZHANG T,PING X.A New Approach to Reliable Detection of LSB Steganography in Natural Images .Signal Processing,2003,83(10):2085-2093.
[29] ZHANG X P,WANG S Z.Statistical Analysis Against Spatial BPCS Steganography .Journal of Computer Aided Design & Computer Graphics,2005,17(7):1625-1629.
[30] ZIOU D,JAFARI R.Efficient Steganalysis of Images:Learning is Good for Anticipation .Pattern Analysis Applications,2014,17(2):279-289.
[31] FRIDRICH J,GOLJAN M,HOGEA D.Steganalysis of JPEG Images:Breaking the F5 Algorithm // Proc.of International Workshop on Information Hiding (IH’02).LNCS,2578,2002:310-323.
[32] HAN X D,PING X J,ZHANG T.Steganalysis Based on the Differences of Coefficient Combinations of 0,1 for Detecting F5 Steganography .Journal of Information Engineering University,2009,10(2):184-187.
[33] PROVOS N,HONEYMAN P.Detecting Steganographic Content on the Internet // Proc.of ISOC NDSS’02.2002:408-412.
[34] ZHANG T,PIRLG X.A Fast and Effective Steganalytic Technique Against Jsteg-like Algorithms // Proc.of 2003 ACM Symposium on Applied Computing.ACM Press,2003:307-311.
[35] LEE K,WESTFELD A,LEE S.Generalized Category Attack - Improving Histogram-based Attack on JPEG LSB Embedding // Proc.of 9th Information Hiding Workshop.Springer,LNCS,2007:35-48.
[36] FRIDRICH J,GOLJAN M,HOGEA D.New Methodology for Breaking Steganographic Techniques for JPEGs // Proc.of IS&T/SHE Electronic Imaging:Security and Watermarking of Multimedia Contents V.SPIE,2003:143-155.
[37] LYU S,FARID H.Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines // Proc.IH’02,Springer-Verlag.LNCS,2002:340-354.
[38] FARID H,LYU S.Higher-order Wavelet Statistics and Their Application to Digital Forensics // Computer Vision and Pattern Recognition Workshop (CVPRW’03).2003:94-94.
[39] HE J H,LIANG X P,LI J Q,et al.Image Steganalysis Based on Bit-Plane Statistical Correlation Using Support Vector Machine .Acta Scientiarum Naturalium Universitatis Sunyatseni,2004,43(sup2):17-20.
[40] PEVNY T,BAS P,FRIDRICH J.Steganalysis by Subtractive Pixel AdJacency Matrix .IEEE Transactions on Information Forensics and Security,2010,5(2):215-224.
[41] FRIDRICH J,KODOVSKY J.Rich Models for Steganalysis of Digital Images .IEEE Transactions on Information Forensics and Security,2012,7(3):868-882.
[42] HOLUB V,FRIDRICH J.Random ProJections of Residuals for Digital Image Steganalysis.IEEE Transactions on Information Forensics and Security,2013,8(12):1996-2006.
[43] WHITAKER J M,KER A D.Steganalysis of Overlapping Images .Proceedings of SPIE,vol.9409,Media Watermarking,Security,and Forensics,2015,94090X.
[44] AVCIBAS I,MEMON N,SANKUR B.Steganalysis Using Image Quality Metrics .IEEE Transactions on Image Processing,2003,12(2):221-229.
[45] SHI Y,CHEN C,CHEN W.A Markov Process based Approach to Effective Attacking JPEG Steganography // Proc.of 8th International Workshop on Information Hiding (IH’2006).Springer,LNCS,2007:249-264.
[46] PEVNY T,FRIDRICH J.Merging Markov and DCT Features for Multi-Class JPEG Steganalysis // Proc.of SPIE Electronic Imaging.Photonics West,2007:3-4.
[47] KODOVSKY J,FRIDRICH J.Calibration revisited // Proceedings of the 11th ACM Workshop on Multimedia and Security (MM&Sec’09).New York,ACM,2009:63-74.
[48] GUAN J B.Research and Implementation of Steganalysis for JPEG Images .Guilin:Guilin University of Electronic Technology,2013.
[49] KODOVSKY J,FRIDRICH J.Steganalysis of JPEG Images Using Rich Models // Proc.of SPIE Electronic Imaging,Media Watermarking.Security,and Forensics,2012:1-13.
[50] HULOB V,FRIDRICH J.Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT .IEEE Transactions on Information Forensics and Security,2015,10(2):219-228.
[51] SONG X,LIU F,YANG C,et al.Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters // Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec’15).New York:ACM,2015:15-23.
[52] WANG C,FENG G.Calibration-based Features for JPEG Steganalysis Using Multi-level Filter // Proc.of IEEE International Conference on Signal Processing,Communications and Computing (ICSPCC 2015).2015.
[53] SAJEDI H.Adaptive Image Steganalysis .Multimedia Tools and Applications,2018,77(13):17269-17284.
[54] FRIDRICH J.Feature-based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes // Proc.of 6th Information Hiding Workshop.Springer,LNCS,2004:67-81.
[55] FU D,SHI Y Q,ZOU D,et al.JPEG Steganalysis Using Empirical Transition Matrix in Block DCT Domain // International Workshop on Multimedia Signal Processing (MMSP’2006).2006:310-313.
[56] DONG J,WANG W,TAN T N.Multi-Class Blind Steganalysis Based on Image Run-Length Analysis // Proc.of International Workshop on Digital Watermarking (IWDW’09).LNCS,2009:199-210.
[57] XU M.Steganalysis for JPEG Image Based on SVM .Changsha:Hunan University,2012.
[58] WANG L N,WANG H S,ZHAI L M,et al.A Blind Steganalytic Method to Detect JPEG Image Steganography .Journal of Wuhan University (Nature Science Edition),2018,64(3):217-224.
[59] BABU J,RANGU S,MANOGNA P.A Survey on Different Feature Extraction and Classification Techniques Used in Image Steganalysis .Journal of Information Security,2017,8(3):186-202.
[60] XU G,WU H,SHI Y.Structural Design of Convolutional Neural Networks for Steganalysis .IEEE Signal Processing Letters,2016,23(5):708-712.
[61] YE J,NI J,YI Y.Deep Learning Hierarchical Representations for Image Steganalysis .IEEE Transactions on Information Forensics and Security,2017,12(11):2545-2557.
[62] WU S,ZHONG S,LIU Y.Deep residual learning for image steganalysis .Multimedia Tools and Applications,2018,77(9):10437-10453.
[63] GAO P X,WEI L X,LIU J,et al.Image Steganalysis Based on Deep Residual Neural Network .Computer Engineering and Design,2018,39(10):3045-3049.
[64] QIN B.JPEG Images Steganalysis Research Based on Bayes Decision .Shenyang:Northeastern University of China,2011.
[65] KODOVSKY J,FRIDRICH J,HOLUB V.Ensemble Classifiers for Steganalysis of Digital Media .IEEE Transactions on Information Forensics and Security,2012,7(2):432-444.
[66] MA Y,LUO X,LI X,et al.Selection of Rich Model Steganalysis Features Based on Decision Rough Set α-Positive Region Reduction .IEEE Transactions on Circuits and Systems for Video Technology,2019,29(2):336-350.
[67] ADELI A,BROUMANDNIA A.Image Steganalysis Using Improved Particle Swarm Optimization Based Feature Selection .Applied Intelligence,2018,48(6):1609-1622.
[68] WU M Q,ZHU Z L,JIN S Y.Secret Key Estimation in Sequential Steganography Based on the Laplacian Model .Computer Engineering & Science,2008,30(2):9-14.
[69] CHAUMONT M.Deep Learning in Steganography and Steganalysis from 2015 to 2018 .Draft,Montpellier University,2019.
[1] LENG Dian-dian, DU Peng, CHEN Jian-ting, XIANG Yang. Automated Container Terminal Oriented Travel Time Estimation of AGV [J]. Computer Science, 2022, 49(9): 208-214.
[2] NING Han-yang, MA Miao, YANG Bo, LIU Shi-chang. Research Progress and Analysis on Intelligent Cryptology [J]. Computer Science, 2022, 49(9): 288-296.
[3] LI Yao, LI Tao, LI Qi-fan, LIANG Jia-rui, Ibegbu Nnamdi JULIAN, CHEN Jun-jie, GUO Hao. Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network [J]. Computer Science, 2022, 49(8): 257-266.
[4] ZHANG Guang-hua, GAO Tian-jiao, CHEN Zhen-guo, YU Nai-wen. Study on Malware Classification Based on N-Gram Static Analysis Technology [J]. Computer Science, 2022, 49(8): 336-343.
[5] LI Bin, WAN Yuan. Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment [J]. Computer Science, 2022, 49(8): 86-96.
[6] HE Qiang, YIN Zhen-yu, HUANG Min, WANG Xing-wei, WANG Yuan-tian, CUI Shuo, ZHAO Yong. Survey of Influence Analysis of Evolutionary Network Based on Big Data [J]. Computer Science, 2022, 49(8): 1-11.
[7] CHEN Ming-xin, ZHANG Jun-bo, LI Tian-rui. Survey on Attacks and Defenses in Federated Learning [J]. Computer Science, 2022, 49(7): 310-323.
[8] HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78.
[9] WANG Fei, HUANG Tao, YANG Ye. Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion [J]. Computer Science, 2022, 49(6A): 784-789.
[10] LI Ya-ru, ZHANG Yu-lai, WANG Jia-chen. Survey on Bayesian Optimization Methods for Hyper-parameter Tuning [J]. Computer Science, 2022, 49(6A): 86-92.
[11] ZHAO Lu, YUAN Li-ming, HAO Kun. Review of Multi-instance Learning Algorithms [J]. Computer Science, 2022, 49(6A): 93-99.
[12] KANG Yan, WANG Hai-ning, TAO Liu, YANG Hai-xiao, YANG Xue-kun, WANG Fei, LI Hao. Hybrid Improved Flower Pollination Algorithm and Gray Wolf Algorithm for Feature Selection [J]. Computer Science, 2022, 49(6A): 125-132.
[13] XIAO Zhi-hong, HAN Ye-tong, ZOU Yong-pan. Study on Activity Recognition Based on Multi-source Data and Logical Reasoning [J]. Computer Science, 2022, 49(6A): 397-406.
[14] YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang. Android Malware Detection Method Based on Heterogeneous Model Fusion [J]. Computer Science, 2022, 49(6A): 508-515.
[15] XU Jie, ZHU Yu-kun, XING Chun-xiao. Application of Machine Learning in Financial Asset Pricing:A Review [J]. Computer Science, 2022, 49(6): 276-286.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!