融合复合纹理特征的图像拼接检测
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摘要
为解决传统图像拼接检测算法对图像内容、光照变化等鲁棒性不强问题,提出一种基于多种纹理特征融合的图像拼接检测方法。对二维灰度图像执行非下采样轮廓波变换(NSCT),以获得包含图像纹理特征的一系列子带图像。对在水平和垂直方向进行差分处理的低频子带图像以及4个高频图像,获取韦伯局部描述符(WLD)纹理和局部三值模式(LTP)纹理。将WLD纹理与灰度共生矩阵结合,得到像素点强度、梯度与灰度之间的关系;再将LTP纹理与灰度共生矩阵结合,得到无噪声和光照影响的像素点灰度间关系;最后分别提取WLD值共生矩阵和LTP值共生矩阵的对比度、相关性、相异度、熵、能量等5个特征,并融合成特征向量,使用RBF神经网络分类。该方法在哥伦比亚彩色图像库上检测准确率达到了95.7%。
Aiming at the problem that traditional image splicing detection algorithm is not robust to image content and illumination change,an image splicing detection method based on multi-texture feature fusion is proposed.In this method,non-subsampling contour wave transform(NSCT)is performed on two-dimensional gray images to obtain a series of sub-band images with image texture features.Then the low frequency sub-band images were processed in the horizontal and vertical directions,and the four high frequency images were used to obtain the Weber local descriptor(WLD)texture and local three-valued model(LTP)texture.Then WLD texture and gray level co-occurrence matrix are combined to obtain the relationship among pixel intensity,gradient and grayscale;LTP texture and grayscale co-occurrence matrix are combined to obtain the relationship among the noiseless pixels affected by light.Finally the five features including WLD value symbiosis matrix,LTP co-occurrence matrix phase contrast ratio,correlation,entropy and energy are extracted and integrated into feature vectors.RBF neural network is employed for classification.
Aiming at the problem that traditional image splicing detection algorithm is not robust to image content and illumination change,an image splicing detection method based on multi-texture feature fusion is proposed.In this method,non-subsampling contour wave transform(NSCT)is performed on two-dimensional gray images to obtain a series of sub-band images with image texture features.Then the low frequency sub-band images were processed in the horizontal and vertical directions,and the four high frequency images were used to obtain the Weber local descriptor(WLD)texture and local three-valued model(LTP)texture.Then WLD texture and gray level co-occurrence matrix are combined to obtain the relationship among pixel intensity,gradient and grayscale;LTP texture and grayscale co-occurrence matrix are combined to obtain the relationship among the noiseless pixels affected by light.Finally the five features including WLD value symbiosis matrix,LTP co-occurrence matrix phase contrast ratio,correlation,entropy and energy are extracted and integrated into feature vectors.RBF neural network is employed for classification.
引文
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[16]COLUMBIA DVMM RESEARCH LAB.Columbia image splicing detection evaluation dataset[EB/OL].http://www.ee.columbia.edu/ln/dvmm/downloads/authsplcuncmp/.
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[2]陈园园,于在河.数字图像被动取证技术综述[J].吉林大学学报:信息科学版,2014,32(6):689-698.
[3]石泽男.基于局部纹理特征的数字图像拼接盲鉴别算法研究[D].长春:吉林大学,2017.
[4]MUHAMMAD H,SAHAR Q,GEORGE B,et al.Evaluation of image forgery detection using multi-scale Weber local descriptors[J].Artificial Intelligence Tools,2015,24(4):416-417.
[5]VERMA M,RAMAN B.Local tri-directional patterns:a new texture feature descriptor for image retrieval[J].Digital Signal Processing,2016,51:62-72.
[6]HASHMI M F,KESKAR A G.Image forgery authentication and classification using hybridization of HMM and SVM classifier[J].International Journal of Security&Its Applications,2015,9(4):125-140.
[7]李燕,钟磊,李健.基于LBP和共生矩阵的图像拼接篡改检测[J].武汉大学学报:理学版,2015,61(6):517-524.
[8]JIN H,LIU Q,LU H,et al.Face detection using improved LBP under Bayesian framework[C].IEEE First Symposium on Multi-Agent Security and Survivability,2004:306-309.
[9]TAN X,XU T,TAN X,et al.Enhanced local texture feature sets for face recognition under difficult lighting conditions[J].IEEE Transactions on Image Processing,2010,19:1635-1650.
[10]JALA T,PETIKAINEN M,MAENPAA T.Multiresolution gray scale and rotation invariant texture classification with local binary patterns[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.
[11]CHEN J,SHAN S G,HE C,et al.WLD:a robust local image descriptor[J].IEEE TPAMI,2010,32(9):1705-1719.
[12]JAIN A K.Fundamentals of digital signal processing[M].New Jersy:Prentice-Hall,1986.
[13]朱正礼,赵春霞,侯迎坤,等.基于多特征的旋转不变纹理图像检索[J].南京理工大学学报,2012,36(3):375-380.
[14]雷升锴,刘红阳,何嘉,等.动态K-均值聚类算法在RBF神经网络中心选取中的应用[J].信息系统工程,2011(6):83-85.
[15]王奇平,王祯璋.基于径向基神经网络的变压器故障诊断方法研究[J].电力与能源,2017,38(5):546-548.
[16]COLUMBIA DVMM RESEARCH LAB.Columbia image splicing detection evaluation dataset[EB/OL].http://www.ee.columbia.edu/ln/dvmm/downloads/authsplcuncmp/.
[17]王任华,霍宏涛,蒋敏.RANSAC算法在同图复制鉴定中的应用研究[J].计算机应用研究,2014,31(7):2209-2211.