计算机科学 ›› 2019, Vol. 46 ›› Issue (6): 174-179.doi: 10.11896/j.issn.1002-137X.2019.06.026

• 信息安全 • 上一篇    下一篇

基于人工蜂群算法的两阶段图像隐写分析算法

穆晓芳1, 邓红霞2, 李晓宾3, 赵鹏4   

  1. (太原师范学院计算机系 太原030619)1
    (太原理工大学信息与计算机学院 太原030024)2
    (北京航空航天大学计算机学院 北京100191)3
    (中国社会科学院 北京100732)4
  • 收稿日期:2018-11-20 发布日期:2019-06-24
  • 通讯作者: 穆晓芳(1974-),女,硕士,副教授,硕士生导师,CCF会员,主要研究领域为分布式计算、图像处理、数据分析,E-mail:mu_xiao_fang@163.com
  • 作者简介:邓红霞(1976-),女,博士,副教授,硕士生导师,CCF会员,主要研究领域为智能信息处理、脑认知研究、图像处理;李晓宾(1991-),男,博士生,主要研究领域为深度压缩、嵌入式智能硬件和视频图像处理;赵 鹏(1973-),男,博士生,教授,硕士生导师,CCF会员,主要研究领域为软件工程、数据分析、算法研究。
  • 基金资助:
    国家自然科学基金项目(F020308),山西省重点研发计划项目(201803D31055),山西省自然科学基金项目(201801D121135)资助。

Two-phase Image Steganalysis Algorithm Based on Artificial Bee Colony Algorithm

MU Xiao-fang1, DENG Hong-xia2, LI Xiao-bin3, ZHAO Peng4   

  1. (Department of Computer Science,Taiyuan Normal University,Taiyuan 030619,China)1
    (College of Information and Computer,Taiyuan University of Technology,Taiyuan 030024,China)2
    (School of Computer Science and Engineering,Beihang University,Beijing 100191,China)3
    (Chinese Academy of Social Sciences,Beijing 100732,China)4
  • Received:2018-11-20 Published:2019-06-24

摘要: 为了提高图像隐写分析的检测准确率,提出了一种基于人工蜂群算法的两阶段图像隐写分析算法。第一阶段,设计了基于模糊理论的隐写模式检测算法,检测部分已知隐写算法的隐写内容;第二阶段,基于人工蜂群算法分析了含密图像的区域与密度双重特征,通过双重特征的分析检测未知隐写算法的嵌入内容。基于公开隐写图像数据集的实验结果表明,所提的两阶段隐写分析算法可获得较高的检测率,同时具有理想的计算效率。

关键词: 多特征分析, 邻接像素, 模糊理论, 人工蜂群算法, 图像隐写分析

Abstract: In order to improve the detection accuracy of the image steganalysis,this paper proposed a two-phase image steganalysis algorithm based on Artificial Bee Colony.In the first phase,steganography pattern detection algorithm based on fuzzy theory is designed to discover steganography content of some known steganography algorithms.In the second phase,dual features of regions and density of stego images are analyzed based on Artificial Bee Colony algorithm,and the embedded content of unknown steganography algorithms is analyzed by dual features.Experimental results on the public steganography images show that the proposed algorithm performs high detection accuracy,and it has desirable computational efficiency.

Key words: Adjacent pixels, Artificial Bee Colony algorithm, Fuzzy theory, Image steganalysis, Multi-feature analysis

中图分类号: 

  • TP391
[1]SI Y F,WEI L X,ZHANG Y N,et al.Revised Steganography Scheme Based on SI-UNIWARD[J].Computer Science,2016,43(5):108-112.
[2]SUN X,ZHANG W M,YU N H,et al.Steganography based on parameters’ disturbance of spatial image transform[J].Journal on Communications,2017,38(10):166-174.
[3]ZHANG Y W,ZHANG W M,YU N H.Specific Testing Sample Steganalysis[J].Journal of Software,2018,29(4):987-1001.
[4]ZHANG Y,LIU F,YANG C,et al.Steganalysis of content-adaptive JPEG steganography based on Gauss partial derivative filter bank[J].Journal of Electronic Imaging,2017,26(1):013011.
[5]JIAN Y,NI J,YANG Y.Deep Learning Hierarchical Representations for Image Steganalysis[J].IEEE Transactions on Information Forensics & Security,2017,12(11):2545-2557.
[6]ZENG J,TAN S,LI B,et al.Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework[J].IEEETransa-ctions on Information Forensics & Security,2018,13(5):1200-1214.
[7]KARAMPIDIS K,KAVALLIERATOU E,PAPADOURAKIS G. A review of image steganalysis techniques for digital forensics[J].Journal of Information Security & Applications,2018,40(4):217-235.
[8]WANG Y J,NIU K,YANG X Y.Information hiding scheme based on generative adversarial network[J].Journal of Computer Applications,2018,38(10):2923-2928.
[9]DUAN R,CHEN D.Video steganography algorithm uses motion vector difference as carrier[J].Journal of Image andGraphi-cs,2018,23(2):163-173.
[10]CAO Z,ZHANG M Q,SUN W J,et al.Novel Steganalysis Algorithm Combine Rotating Forest Transformation with Multiple Classifi-ers Ensemble[J].Journal of Chinese Computer Systems,2017,38(10):2297-2302.
[11]HAO Z,TAO Z,CHEN H.Revisiting weighted Stego-image Steganalysis for PVD steganography[J].Multimedia Tools & Applications,2018,3(2):1-19.
[12]SONG X,LIU F,LUO X,et al.Steganalysis of perturbed quantization steganography based on the enhanced histogram features[J].Multimedia Tools and Applications,2015,74(24):11045-11071.
[13]SURYAWANSHI G R,MALI S N.Universal steganalysis using IQM and multiclass discriminator for digital images[C]∥International Conference on Signal Processing.2017.
[14]WU S,ZHONG S,LIU Y.Deep residual learning for image steganalysis[J].Multimedia Tools & Applications,2017,77(9):1-17.
[15]HAO Z,PING X J,MANKUN X U,et al.Steganalysis by subtractive pixel adjacency matrix and dimensionality reduction[J].Science China Information Sciences,2014,57(4):1-7.
[16]BOROUMAND M,FRIDRICH J.Applications of Explicit Non-Linear Feature Maps in Steganalysis[J].IEEE Transactions on Information Forensics & Security,2018,13(4):823-833.
[17]NOURI R,MANSOURI A.Blind image steganalysis based on reciprocal singular value curve[C]∥Iranian Conference on Machine Vision and Image Processing.IEEE,2015:124-127.
[18]CHANG K K,HUO J Y,MEI K.A Gbest-Guided Aritificial Bee Colony Algorithm with Hunting Factor .Journal of Chongqing University of Technology(Natural Science) ,2017(6):160-165,187.(in Chinese)
常扣扣,火久元,梅凯.一种带搜索因子的全局最优人工蜂群算法.重庆理工大学学报(自然科学版),2017(6):160-165,187.
[1] 吴功兴, 孙兆洋, 琚春华.
考虑中断风险与模糊定价的闭环供应链网络设计模型
Closed-loop Supply Chain Network Design Model Considering Interruption Risk and Fuzzy Pricing
计算机科学, 2022, 49(7): 220-225. https://doi.org/10.11896/jsjkx.201100084
[2] 石克翔, 保利勇, 丁洪伟, 官铮, 赵雷.
基于生成时间序列均匀优化的混沌人工蜂群算法
Chaos Artificial Bee Colony Algorithm Based on Homogenizing Optimization of Generated Time Series
计算机科学, 2021, 48(7): 270-280. https://doi.org/10.11896/jsjkx.200800087
[3] 陈海彪, 黄声勇, 蔡洁锐.
一个基于智能电网的跨层路由的信任评估协议
Trust Evaluation Protocol for Cross-layer Routing Based on Smart Grid
计算机科学, 2021, 48(6A): 491-497. https://doi.org/10.11896/jsjkx.201000169
[4] 刘漳辉, 赵旭, 林兵, 陈星.
混合云环境下基于模糊理论的科学工作流数据布局策略
Data Placement Strategy of Scientific Workflow Based on Fuzzy Theory in Hybrid Cloud
计算机科学, 2021, 48(11): 199-207. https://doi.org/10.11896/jsjkx.200900009
[5] 郑友莲, 雷德明, 郑巧仙.
求解高维多目标调度的新型人工蜂群算法
Novel Artificial Bee Colony Algorithm for Solving Many-objective Scheduling
计算机科学, 2020, 47(7): 186-191. https://doi.org/10.11896/jsjkx.190600089
[6] 彭伟, 胡宁, 胡璟璟.
图像隐写分析算法研究概述
Overview of Research on Image Steganalysis Algorithms
计算机科学, 2020, 47(6A): 325-331. https://doi.org/10.11896/JsJkx.190600103
[7] 郭佳.
基于改进的人工神经网络对存储系统性能进行预测的方法
Method of Predicting Performance of Storage System Based on Improved Artificial Neural Network
计算机科学, 2019, 46(6A): 52-55.
[8] 符晓.
云计算中基于共享机制和群体智能优化算法的任务调度方案
Task Scheduling Scheme Based on Sharing Mechanism and Swarm Intelligence
Optimization Algorithm in Cloud Computing
计算机科学, 2018, 45(6A): 290-294.
[9] 范兴刚, 刘涛, 胡凤丹, 蒿翔.
一种延长目标覆盖网络寿命的群智能算法
Swarm Intelligence Algorithm for Prolonging Target Coverage Network Lifetime
计算机科学, 2018, 45(12): 86-91. https://doi.org/10.11896/j.issn.1002-137X.2018.12.013
[10] 火久元, 王野, 胡卓娅.
人工蜂群算法的收敛性分析:数形结合
Convergence Analysis of Artificial Bee Colony Algorithm:Combination of Number and Shape
计算机科学, 2018, 45(10): 212-216. https://doi.org/10.11896/j.issn.1002-137X.2018.10.039
[11] 李贞,张卓,王黎明.
基于三元概念分析的文本分类算法研究
Research on Text Classification Algorithm Based on Triadic Concept Analysis
计算机科学, 2017, 44(8): 207-215. https://doi.org/10.11896/j.issn.1002-137X.2017.08.036
[12] 杨华,张杭,张江,杨柳,李炯.
初始分离矩阵优化的在线盲源分离算法
Initial Separating-matrix Optimized Online Blind Source Separation Algorithm
计算机科学, 2016, 43(Z6): 265-267. https://doi.org/10.11896/j.issn.1002-137X.2016.6A.063
[13] 邢熔华,黄海燕.
基于改进全局人工蜂群算法的WSN节点定位研究
Researches on Wireless Sensor Network Localization Based on Improved Gbest-guided Artificial Bee Colony Algorithm
计算机科学, 2016, 43(12): 273-276. https://doi.org/10.11896/j.issn.1002-137X.2016.12.050
[14] 李仁兴,丁力.
基于云模型蜂群算法的无人机航迹规划
Path Planning for Unmanned Air Vehicles Using Improved Artificial Bee Colony Algorithm
计算机科学, 2015, 42(Z11): 89-92.
[15] 钟夫,郭建胜,张斯嘉,王族统.
基于优化支持向量机的供应链竞争力评价方法
Supply Chain Competitiveness Evaluation Method Based on Optimized Support Vector Machine
计算机科学, 2015, 42(Z11): 27-31.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!