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摘要: 现有的复制-粘贴盲鉴别算法大多忽略图像彩色信息，导致对隐蔽性篡改方式的检测率较低，基于此，本文提出一种基于彩色局部二值模式（Color local binary patterns，CoLBP）的隐蔽性复制-粘贴盲鉴别算法.算法首先对彩色图像进行预处理，即建立彩色LBP纹理图像，从而实现彩色信息与LBP纹理特征的融合；其次重叠分块并提取灰度共生矩阵（Gray level co-occurrence matrix，GLCM）特征；最后，提出改进的kd树和超平面划分标记split搜索方法，快速匹配图像块，并应用形态学操作去除误匹配，精确定位复制-粘贴区域.实验结果表明，本算法对隐蔽性复制-粘贴篡改定位准确，并对模糊、噪声、JPEG重压缩后处理操作有很好的鲁棒性.Abstract: Since negligence of color information in detecting copy-move forgeries leads to low accuracy in detection of in covert tampering, a novel method using color local binary patterns (CoLBP) is proposed. It involves the following three steps: first, establish color LBP texture image, which is a preprocessing of image and a combination of color information and LBP texture; second, divide into overlapping blocks and extract gray level co-occurrence matrix (GLCM) features; finally, match image blocks by the improved kd tree and split partition, remove the false matched blocks using morphological operation and then detect the resulting copy-move regions. Experimental results show that our algorithm is effective for covert tampering, and exhibits high robustness even when an image is distorted by blur, noise and JPEG recompression.
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图 1 复制-粘贴篡改分类示例 ((a) 隐蔽性复制-粘贴篡改图像示例; (b) 造成假象类复制-粘贴篡改图像示例, 其中实线和虚线矩形框分别为复制和篡改区域)
Fig. 1 The exemplar of copy-move forgery classify ((a) The exemplar of covert copy-move forgery; (b) The exemplar of spurious copy-move forgery, where solid and dashed rectangles are copied and pasted regions.)
图 4 GLCM特征提取示意图 (其中实线分别表示o=1~4, s=1;虚线表示o=1, s=2)
Fig. 4 The diagram of GLCM feature extraction, where solid line are respectively indicate o=1~4, s=1; dash line indicates o=1, s=2
图 6 彩色LBP图像的有效性实验示例 ((a) 直接在灰度图像上提取GLCM特征匹配结果; (b) 本文算法匹配结果, 其中标记区域为篡改区域)
Fig. 6 The experiment on effectiveness of color LBP ((a) The matching result based on directly extracting GLCM feature on gray image; (b) The matching result based on our method, where marked regions are forged regions.)
图 8 隐蔽性复制-粘贴篡改检测结果示例 ((a), (g) 隐蔽性复制-粘贴篡改图像; (b), (h) 彩色LBP图像; (c), (i) DCT[4]算法检测结果; (d), (j) Zernike[14]算法检测结果; (e), (k) LBP[17]算法检测结果; (f), (l) 本文算法检测结果.其中, (a), (g) 实线和虚线框分别表示复制和粘贴区域; (c)~(f), (i)~(l) 标记区域为算法检测篡改区域)
Fig. 8 The exemplar results on covert copy-move forgery detection ((a), (g) Covert copy-move forged images; (b), (h) Color LBP images; (c), (i) The results based on DCT[4]; (d), (j) The results based on Zernike[14]; (e), (k) The results based on LBP[17]; (f), (l) The results based on our method. Where (a), (g) solid and dashed rectangles are copied and pasted regions; (c)~(f), (i)~(l) marked regions are detected forged regions.)
图 9 造成假象类复制-粘贴篡改检测结果示例 ((a), (g) 造成假象类复制-粘贴篡改图像; (b), (h) 彩色LBP图像; (c), (i) DCT[4]算法检测结果; (d), (j) Zernike[14]算法检测结果; (e), (k) LBP[17]算法检测结果; (f), (l) 本文算法检测结果.其中, (a), (g) 实线和虚线框分别表示复制和粘贴区域; (c)~(f), (i)~(l) 标记区域为算法检测篡改区域)
Fig. 9 The exemplar results on spurious copy-move forgery detection ((a), (g) Spurious copy-move forged images; (b), (h) Color LBP images; (c), (i) The results based on DCT[4]; (d), (j) The results based on Zernike[14]; (e), (k) The results based on LBP[17]; (f), (l) The results based on our method. Where (a), (g) solid and dashed rectangles are copied and pasted regions; (c)~(f) marked regions are detected forged regions; (i)~(l) green regions are detected forged regions.)
图 12 添加压缩因子90的JPEG重压缩实验结果
Fig. 12 The results of adding JPEG compression with quality factor 90
图 13 后处理操作ROC曲线图 ((a)~(c) 分别为添加不同程度的高斯白噪声、高斯模糊和JPEG重压缩的ROC曲线)
Fig. 13 The ROC curves of post-processing operations (Where (a)~(c) are adding different degrees of Gaussian noise, Gaussian blur, and JPEG compression.)
图 14 存在相似目标的真实图像示例
Fig. 14 The exemplar of authentic images with similar but genuine objects
表 1 彩色空间选择分析
Table 1 The analysis on color space choice
彩色空间 彩色LBP图像提取时间 (s) TPR (%) FPR (%) RGB 4 95 9 LAB 5 94 11 HSV 5.5 95 10 
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表 2 灰度级别gth选择
Table 2 The choice of gray level gth
灰度级别$g_{th}$ 特征维度 TPR (%) FPR (%) 4 16 85 15 8 64 91 13 16 256 95 7 32 024 95 6
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[1] Fridrich A J, Soukal B D, Lukáš A J. Detection of copy-move forgery in digital images. In: Proceedings of the 2003 Digital Forensic Research Workshop. Cleveland, USA, 2003. [2] Wang X, Zhang X, Li Z, Wang S. A DWT-DCT based passive forensics method for copy-move attacks. In: Proceedings of the 3rd International Conference on Multimedia Information Networking and Security. Shanghai, China: IEEE, 2011. 304-308 [3] Hu J, Zhang H, Gao Q, Huang H. An improved lexicographical sort algorithm of copy-move forgery detection. In: Proceedings of the 2nd International Conference on Networking and Distributed Computing. Beijing, China: IEEE, 2011. 23-27 [4] Cao Y J, Gao T G, Li F, Yang Q T. A robust detection algorithm for copy-move forgery in digital images. Forensic Science International, 2012, 214(1-3): 33-43 doi: 10.1016/j.forsciint.2011.07.015 [5] Popescu A C, Farid H. Exposing Digital Forgeries by Detecting Duplicated Image Regions, Technical Report 2004-515, Dartmouth College, USA, 2004. [6] 骆伟祺, 黄继武, 丘国平.鲁棒的区域复制图像篡改检测技术.计算机学报, 2007, 30(11): 1998-2007 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJX200711013.htmLuo Wei-Qi, Huang Ji-Wu, Qiu Guo-Ping. Robust detection of region-duplication forgery in digital image. Chinese Journal of Computers, 2007, 30(11): 1998-2007 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJX200711013.htm [7] Muhammad G, Hussain M, Bebis G. Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digital Investigation, 2012, 9(1): 49-57 doi: 10.1016/j.diin.2012.04.004 [8] Bayram S, Sencar H T, Memon N. An efficient and robust method for detecting copy-move forgery. In: Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. Taipei, China: IEEE, 2009. 1053-1056 [9] Zhong J L, Gan Y F. Detection of copy-move forgery using discrete analytical Fourier-Mellin transform. Nonlinear Dynamics, 2016, 84(1): 189-202 doi: 10.1007/s11071-015-2374-9 [10] Zhang T, Wang R. Copy-move forgery detection based on SVD in digital image. In: Proceedings of the 2nd International Congress on Image and Signal Processing. Tianjin, China: IEEE, 2009. 1-5 [11] Liu G J, Wang J W, Lian S G, Wang Z Q. A passive image authentication scheme for detecting region-duplication forgery with rotation. Journal of Network and Computer Applications, 2011, 34(5): 1557-1565 doi: 10.1016/j.jnca.2010.09.001 [12] Mahdian B, Saic S. Detection of copy-move forgery using a method based on blur moment invariants. Forensic Science International, 2007, 171(2-3): 180-189 doi: 10.1016/j.forsciint.2006.11.002 [13] Ryu S J, Lee M J, Lee H K. Detection of copy-rotate-move forgery using Zernike moments. In: Proceedings of the 12th International Conference on Information Hiding. Calgary, AB, Canada: Springer, 2010. 51-65 [14] Ryu S J, Kirchner M, Lee M J, Lee H K. Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Transactions on Information Forensics and Security, 2013, 8(8): 1355-1370 doi: 10.1109/TIFS.2013.2272377 [15] Li L D, Li S S, Zhu H C, Wu X Y. Detecting copy-move forgery under affine transforms for image forensics. Computers and Electrical Engineering, 2014, 40(6): 1951-1962 doi: 10.1016/j.compeleceng.2013.11.034 [16] Lynch G, Shih F Y, Liao H Y M. An efficient expanding block algorithm for image copy-move forgery detection. Information Sciences, 2013, 239: 253-265 doi: 10.1016/j.ins.2013.03.028 [17] Davarzani R, Yaghmaie K, Mozaffari S, Tapak M. Copy-move forgery detection using multiresolution local binary patterns. Forensic Science International, 2013, 231(1-3): 61-72 doi: 10.1016/j.forsciint.2013.04.023 [18] Cozzolino D, Poggi G, Verdoliva L. Efficient dense-field copy-move forgery detection. IEEE Transactions on Information Forensics and Security, 2015, 10(11): 2284-2297 doi: 10.1109/TIFS.2015.2455334 [19] Porebski A, Vandenbroucke N, Macaire L. Haralick feature extraction from LBP images for color texture classification. In: Proceedings of the 1st Workshops on Image Processing Theory, Tools and Applications. Sousse: IEEE, 2008. 1-8 [20] Haralick R M. Statistical and structural approaches to texture. Proceedings of the IEEE, 1979, 67(5): 786-804 doi: 10.1109/PROC.1979.11328 [21] Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G. A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security, 2011, 6(3): 1099-1110 doi: 10.1109/TIFS.2011.2129512 [22] Kakar P, Sudha N. Exposing post processed copy-paste forgeries through transform-invariant features. IEEE Transactions on Information Forensics and Security, 2012, 7(3): 1018-1028 doi: 10.1109/TIFS.2012.2188390 期刊类型引用(15)
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