基于LBC的计算机生成图像盲鉴别算法
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摘要
针对现有的计算机生成图像盲鉴别算法选用的分类特征维度较高、通用性差等问题,提出了一种基于局部二进制计数模式的计算机生成图像盲鉴别算法。首先,将原始图像由RGB颜色空间转换为HSV颜色空间;然后,提取HSV颜色空间图像及其下采样图像的局部二进制计数模式矩阵,求取矩阵归一化直方图;最后,将上述直方图作为分类特征送入SVM分类器,实现计算机生成图像的盲鉴别。实验结果表明,该算法可以有效地鉴别自然图像和计算机生成图像,与现有算法相比具有更高的识别率和较低的特征维度。
Aiming at the problem that the classification features selected by the existing blind identification algorithms of photorealistic computer graphics have high dimensions and poor universalities,this paper put forward a blind identification algorithm of photorealistic computer graphics based on local binary count.First,the original image is converted from RGB color space to HSV color space.Then,the local binary count matrix is extracted from the HSV color space images and its down-sampling image,and the normalized histogram of the matrix is calculated.Finally,the above histogram is sent as classification features into the SVM classifier,implementing the blind identification of photorealistic computer graphics.The experimental results show that the algorithm can effectively identify photographic images and photorealistic computer graphics.Compared with the existing algorithm,it has higher recognition rate and lower feature dimension.
Aiming at the problem that the classification features selected by the existing blind identification algorithms of photorealistic computer graphics have high dimensions and poor universalities,this paper put forward a blind identification algorithm of photorealistic computer graphics based on local binary count.First,the original image is converted from RGB color space to HSV color space.Then,the local binary count matrix is extracted from the HSV color space images and its down-sampling image,and the normalized histogram of the matrix is calculated.Finally,the above histogram is sent as classification features into the SVM classifier,implementing the blind identification of photorealistic computer graphics.The experimental results show that the algorithm can effectively identify photographic images and photorealistic computer graphics.Compared with the existing algorithm,it has higher recognition rate and lower feature dimension.
引文
[1]万国富.基于分形维数的自然图像盲鉴别算法研究[D].长春:吉林大学,2013Wan Guo-fu.Research on natural image blind identifying algorithm based on fractal dimension[D].Changchun:Jilin University,2013
[2]张震,杨宇豪.基于Benford模型的自然图像与计算机生成图像的鉴别[J].北京工业大学学报,2013,39(6):930-935Zhang Zhen,Yang Yu-hao.Distinguishing computer graphics from natural image based on Benford model[J].Journal of Beijing University of Technology,2013,39(6):930-935
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[11]Zhao Y,Huang D,Jia W.Completed local binary count for rotation invariant texture classification[J].IEEE Trans.Image Process.,2012,21(10):4492-4497
[12]李文祥,张涛,郑二功,等.基于二阶差分统计量的自然图像与计算机图形的鉴别[J].计算机辅助设计与图形学学报,2010,22(9):1613-1618Li Wen-xiang,Zhang Tao,Zheng Er-gong,et al.Discrimination between natural images and photorealistic computer graphics using second-order difference statistics[J].Journal of ComputerAided Design&Computer Graphics,2010,22(9):1613-1618
[13]Ker A D.Steganalysis of LSB matching in grayscale images[J].Signal Processing Letters,IEEE,2005,12(6):441-444
[14]Chang C C,Lin C J.LIBSVM:a library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology(TIST),2011,2(3):27
[15]Ng T T,Chang S F,Hsu J,et al.Columbia photographic images and photorealistic computer graphics dataset[R].Columbia University,ADVENT Technical Report,2005:205-2004
[2]张震,杨宇豪.基于Benford模型的自然图像与计算机生成图像的鉴别[J].北京工业大学学报,2013,39(6):930-935Zhang Zhen,Yang Yu-hao.Distinguishing computer graphics from natural image based on Benford model[J].Journal of Beijing University of Technology,2013,39(6):930-935
[3]Lyu S,Farid H.How realistic is photorealistic?[J].IEEE Transactions on Signal Processing,2005,53(2):845-850
[4]郑二功,平西建.一种基于相邻像素一致性的数码照片与计算机图像鉴别方法[J].计算机研究与发展,2009,46(增刊I):258-262Zheng Er-gong,Ping Xi-jian.Identifying computer graphics from digital photographs based on coherence of adjacent pixels[J].Journal of Computer Research and Development,2009,46(Suppl I):258-262
[5]Xu B,Wang J,Liu G,et al.Photorealistic computer graphics forensics based on leading digit law[J].Journal of Electronics(China),2011,28(1):95-100
[6]Wu R,Li X,Yang B.Identifying computer generated graphics via histogram features[C]∥2011 18th IEEE International Conference on Image Processing(ICIP).IEEE,2011:1933-1936
[7]Li Z,Ye J,Shi Y Q.Distinguishing computer graphics from photographic images using local binary patterns[M]∥Digital Forensics and Watermaking.Springer Berlin Heidelberg,2013:228-241
[8]Dehnie S,Sencar T,Memon N.Digital image forensics for identifying computer generated and digital camera images[C]∥2006IEEE International Conference on Image Processing.IEEE,2006:2313-2316
[9]Dirik A E,Bayram S,Sencar H T,et al.New features to identify computer generated images[C]∥IEEE International Conference on Image Processing,2007(ICIP 2007).IEEE,2007,4:433-436
[10]郭克.自然图像和计算机生成图像检测方法研究[D].宁波:宁波大学,2012Guo Ke.Research on the detection methods of natural images and computer-generated images[D].Ningbo:Ningbo University,2012
[11]Zhao Y,Huang D,Jia W.Completed local binary count for rotation invariant texture classification[J].IEEE Trans.Image Process.,2012,21(10):4492-4497
[12]李文祥,张涛,郑二功,等.基于二阶差分统计量的自然图像与计算机图形的鉴别[J].计算机辅助设计与图形学学报,2010,22(9):1613-1618Li Wen-xiang,Zhang Tao,Zheng Er-gong,et al.Discrimination between natural images and photorealistic computer graphics using second-order difference statistics[J].Journal of ComputerAided Design&Computer Graphics,2010,22(9):1613-1618
[13]Ker A D.Steganalysis of LSB matching in grayscale images[J].Signal Processing Letters,IEEE,2005,12(6):441-444
[14]Chang C C,Lin C J.LIBSVM:a library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology(TIST),2011,2(3):27
[15]Ng T T,Chang S F,Hsu J,et al.Columbia photographic images and photorealistic computer graphics dataset[R].Columbia University,ADVENT Technical Report,2005:205-2004