基于人工蜂群算法的两阶段图像隐写分析算法
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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。
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.
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.
引文
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[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 [J].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[J].重庆理工大学学报(自然科学版),2017(6):160-165,187.
[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].IEEE Transa-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 and Graphi-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 [J].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[J].重庆理工大学学报(自然科学版),2017(6):160-165,187.