计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 325-331.doi: 10.11896/JsJkx.190600103

• 信息安全 • 上一篇    下一篇

图像隐写分析算法研究概述

彭伟1, 胡宁2, 胡璟璟1   

  1. 1 国防科技大学计算机学院 长沙 410073;
    2 广州大学网络空间安全研究院 广州 510006
  • 发布日期:2020-07-07
  • 通讯作者: 彭伟(wpeng@nudt.edu.cn)

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.

摘要: 图像隐写技术可以在互联网上传输各种数字图片中隐藏的敏感或秘密信息,在过去二十多年中得到了快速的发展,并被不法分子用来交换可能危害社会安全的信息。为消除这些危害,相应发展了各种图像隐写分析技术。通过检查可疑图片中隐藏的秘密信息,图像隐写分析可以提供数字法理证据。在图像隐写算法发展现状分析的基础上,将图像隐写分析算法分为专用和通用隐写分析算法两大类,对图像隐写分析技术进行了介绍和归纳。在专用算法方面,分别介绍了针对特定图像隐写算法和针对特定图像类型的图像隐写分析途径。在通用算法方面,介绍了基于图像特征的图像隐写分析方法的一般流程,归纳总结了图像隐写分析常用的几类图像特征。通过回顾图像隐写分析的已有工作,分析了图像隐写分析中采用的技术,包括基于机器学习的分类方法、特征选择方法等。最后,对图像隐写分析的未来研究发展方向做了简要的讨论。

关键词: 机器学习, 特征选择, 图像隐写分析, 图像隐写分析算法, 信息隐藏

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

中图分类号: 

  • 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] 冷典典, 杜鹏, 陈建廷, 向阳.
面向自动化集装箱码头的AGV行驶时间估计
Automated Container Terminal Oriented Travel Time Estimation of AGV
计算机科学, 2022, 49(9): 208-214. https://doi.org/10.11896/jsjkx.210700028
[2] 宁晗阳, 马苗, 杨波, 刘士昌.
密码学智能化研究进展与分析
Research Progress and Analysis on Intelligent Cryptology
计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053
[3] 李斌, 万源.
基于相似度矩阵学习和矩阵校正的无监督多视角特征选择
Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment
计算机科学, 2022, 49(8): 86-96. https://doi.org/10.11896/jsjkx.210700124
[4] 李瑶, 李涛, 李埼钒, 梁家瑞, Ibegbu Nnamdi JULIAN, 陈俊杰, 郭浩.
基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究
Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network
计算机科学, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094
[5] 张光华, 高天娇, 陈振国, 于乃文.
基于N-Gram静态分析技术的恶意软件分类研究
Study on Malware Classification Based on N-Gram Static Analysis Technology
计算机科学, 2022, 49(8): 336-343. https://doi.org/10.11896/jsjkx.210900203
[6] 何强, 尹震宇, 黄敏, 王兴伟, 王源田, 崔硕, 赵勇.
基于大数据的进化网络影响力分析研究综述
Survey of Influence Analysis of Evolutionary Network Based on Big Data
计算机科学, 2022, 49(8): 1-11. https://doi.org/10.11896/jsjkx.210700240
[7] 陈明鑫, 张钧波, 李天瑞.
联邦学习攻防研究综述
Survey on Attacks and Defenses in Federated Learning
计算机科学, 2022, 49(7): 310-323. https://doi.org/10.11896/jsjkx.211000079
[8] 胡艳羽, 赵龙, 董祥军.
一种用于癌症分类的两阶段深度特征选择提取算法
Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification
计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092
[9] 李亚茹, 张宇来, 王佳晨.
面向超参数估计的贝叶斯优化方法综述
Survey on Bayesian Optimization Methods for Hyper-parameter Tuning
计算机科学, 2022, 49(6A): 86-92. https://doi.org/10.11896/jsjkx.210300208
[10] 赵璐, 袁立明, 郝琨.
多示例学习算法综述
Review of Multi-instance Learning Algorithms
计算机科学, 2022, 49(6A): 93-99. https://doi.org/10.11896/jsjkx.210500047
[11] 康雁, 王海宁, 陶柳, 杨海潇, 杨学昆, 王飞, 李浩.
混合改进的花授粉算法与灰狼算法用于特征选择
Hybrid Improved Flower Pollination Algorithm and Gray Wolf Algorithm for Feature Selection
计算机科学, 2022, 49(6A): 125-132. https://doi.org/10.11896/jsjkx.210600135
[12] 王飞, 黄涛, 杨晔.
基于Stacking多模型融合的IGBT器件寿命的机器学习预测算法研究
Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion
计算机科学, 2022, 49(6A): 784-789. https://doi.org/10.11896/jsjkx.210400030
[13] 肖治鸿, 韩晔彤, 邹永攀.
基于多源数据和逻辑推理的行为识别技术研究
Study on Activity Recognition Based on Multi-source Data and Logical Reasoning
计算机科学, 2022, 49(6A): 397-406. https://doi.org/10.11896/jsjkx.210300270
[14] 姚烨, 朱怡安, 钱亮, 贾耀, 张黎翔, 刘瑞亮.
一种基于异质模型融合的 Android 终端恶意软件检测方法
Android Malware Detection Method Based on Heterogeneous Model Fusion
计算机科学, 2022, 49(6A): 508-515. https://doi.org/10.11896/jsjkx.210700103
[15] 许杰, 祝玉坤, 邢春晓.
机器学习在金融资产定价中的应用研究综述
Application of Machine Learning in Financial Asset Pricing:A Review
计算机科学, 2022, 49(6): 276-286. https://doi.org/10.11896/jsjkx.210900127
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!