基于最小值滤波与感知哈希算法的图像隐写算法
Image Steganography Algorithm Based on Minimum Filter and Perceptual Hash
DOI: 10.12677/CSA.2020.104078, PDF,    科研立项经费支持
作者: 刘 骞：广西大学计算机与电子信息学院，广西，南宁；李清光*：广西大学计算机与电子信息学院，广西，南宁;广西多媒体通信与网络技术重点实验室，广西 南宁
关键词: 修改聚集；最小值滤波；感知哈希；图像隐写；Clustering Rule； Minimum Filter； Perceptual Hash； Image Steganography
摘要: 现有图像隐写算法没有很好地应用修改聚集原则，导致隐写算法的安全性仍然不够理想。经典隐写算法MiPOD (Minimizing the Power of Optimal Detector)拥有较高的安全性，但它使用随机修改方向的方式对像素进行修改，不符合修改聚集原则。为了提升隐写算法的安全性，在MiPOD的基础上，本文使用最小值滤波将代价矩阵局部区域内最小值的修改方式赋予整个局部，实现局部修改聚集；此外使用感知哈希算法寻找图像中相似的部分，赋予相似区域相同的修改方向，以达到整体修改聚集的目的。实验结果表明，本文算法相较于MiPOD，抵抗SRM特征的检错率平均提升了0.72%，抵抗maxSRM特征的检错率平均提升了0.61%。
Abstract: Now the image steganography algorithm does not apply the clustering rule very well. It leads to the security of steganography algorithm that is still not ideal. MiPOD (Minimizing the Power of Optimal Detector) is a classical steganography algorithm, which has a high security. But it uses the method of randomly changing the direction to modify the pixels, which does not conform to the clustering rule. In order to improve the security of the steganography algorithm, our algorithm which base on MiPOD uses the minimum filter, the pixel changed method corresponding to the minimum value in the local area of the cost matrix is given to the whole local area. It can realize local clustering. In addition, the perceptual hash algorithm is used to find the similar parts of the image, and the similar regions are given the same changed direction. It can realize overall clustering. Comparing with the MiPOD, the testing error rate of SRM is improved by 0.72%, and that of maxSRMd2 is improved by 0.61%.
文章引用：刘骞, 李清光. 基于最小值滤波与感知哈希算法的图像隐写算法[J]. 计算机科学与应用, 2020, 10(4): 749-759. https://doi.org/10.12677/CSA.2020.104078

参考文献

[1] Mukherjee, S. and Sanyal, G. (2019) Edge Based Image Steganography with Variable Threshold. Multimedia Tools and Applications, 78, 16363-16388. [Google Scholar] [CrossRef] 
[2] Li, B., Tan, S., Ming, W., et al. (2014) Investigation on Cost Assignment in Spatial Image Steganography. IEEE Transactions on Information Forensics & Security, 9, 1264-1277. [Google Scholar] [CrossRef] 
[3] Tang, W., Tan, S., Li, B., et al. (2017) Automatic Steganographic Distortion Learning Using a Generative Adversarial Network. IEEE Signal Processing Letters, 24, 1547-1551. [Google Scholar] [CrossRef] 
[4] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al. (2014) Generative Adversarial Nets. International Conference on Neural Information Processing Systems, 2672-2680.
[5] Tang, W., Li, B. and Tan, S. (2019) CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography. IEEE Transactions on Information Forensics and Security, 14, 2074-2087. [Google Scholar] [CrossRef] 
[6] Holub, V. and Fridrich, J. (2012) Designing Steganographic Distortion Using Directional Filters. International Workshop on Information Forensics and Security (WIFS), Tenerife, Spain, 234-239. [Google Scholar] [CrossRef] 
[7] Fridrich, J. and Kodovsky, J. (2013) Multivariate Gaussian Model for Designing Additive Distortion for Steganography: 2013 IEEE International Conference on Acoustics, Vancouver, 2949-2953. [Google Scholar] [CrossRef] 
[8] Sedighi, V., Fridrich, J. and Cogranne, R. (2015) Content-Adaptive Pentary Steganography Using the Multivariate Generalized Gaussian Cover Model. Proceedings of SPIE-The International Society for Optical Engineering, 9409, 1-13. [Google Scholar] [CrossRef] 
[9] Sedighi, V., Cogranne, R. and Fridrich, J. (2016) Content-Adaptive Steganography by Minimizing Statistical Detectability. IEEE Transactions on Information Forensics & Security, 11, 221-234. [Google Scholar] [CrossRef] 
[10] Li, B., Ming, W., Huang, J., et al. (2015) A New Cost Function for Spatial Image Steganography. 2015 IEEE International Conference on Image Processing, Quebec City, Canada, 27-30 September 2015, 4206-4210.
[11] Pevny, T., Bas, P. and Fridrich, J. (2010) Steganalysis by Subtractive Pixel Adja-cency Matrix. IEEE Transactions on Information Forensics & Security, 5, 215-224. [Google Scholar] [CrossRef] 
[12] Pevný, T. and Fridrich, J. (2008) Benchmarking for Steganography. [Google Scholar] [CrossRef] 
[13] 郑传声. 基于最小化嵌入失真的空域图像隐写算法研究[D]: [硕士学位论文]. 北京: 北京邮电大学, 2018.
[14] Li, B., Ming, W., Li, X., et al. (2015) A Strategy of Clustering Modification Directions in Spatial Image Steganography. IEEE Transactions on Information Forensics & Security, 10, 1905-1917. [Google Scholar] [CrossRef] 
[15] Subhedar, M.S. and Mankar, V.H. (2019) Image Steganography Us-ing Contourlet Transform and Matrix Decomposition Techniques. Multimedia Tools and Applications, 78, 22155-22181. [Google Scholar] [CrossRef] 
[16] 张立国, 王松, 金梅, 等. 基于多尺度感知哈希特征的目标跟踪算法研究[J]. 高技术通讯, 2018, 28(3): 39-46.
[17] Ker, A.D. (2009) Estimating Steganographic Fisher Information in Real Images. In: Katzenbeisser, S. and Sadeghi, A.R., Eds., Information Hiding. IH 2009. Lecture Notes in Computer Science, Volume 5806, Springer, Berlin, Heidelberg, 73-88. [Google Scholar] [CrossRef] 
[18] Bas, P., Filler, T. and Pevný, T. (2011) Break Our Steganographic System: The Ins and Outs of Organizing BOSS. In: Proceedings of International Conference on Information Hiding, Prague, Czech Republi, Springer-Verlag, Berlin, 58-70. [Google Scholar] [CrossRef] 
[19] Kodovský, J. and Fridrich, J. (2012) Rich Models for Steganalysis of Digital Images. IEEE Transactions on Information Forensics and Security, 7, 868-882. [Google Scholar] [CrossRef] 
[20] Denemark, T., Sedighi, V., Holub, V., et al. (2014) Selection-Channel-Aware Rich Model for Steganalysis of Digital Images. 2014 IEEE International Workshop on Information Forensics and Security (WIFS), Atlanta, GA, 48-53. [Google Scholar] [CrossRef] 
[21] Kodovský, J., Fridrich, J. and Holub, V. (2012) Ensemble Classifiers for Steganalysis of Digital Media. IEEE Transactions on Information Forensics & Security, 7, 432-444. [Google Scholar] [CrossRef] 
[22] Abdulla, A.A., Sellahewa, H. and Jassim, S.A. (2019) Improving Embedding Efficiency for Digital Steganography by Exploiting Similarities between Secret and Cover Images. Multi-media Tools and Applications, 78, 17799-17823. [Google Scholar] [CrossRef]