利用通道间相关性的CFA图像盲取证

doi:10.3969/j.issn.0255-8297.2015.01.010

应用科学学报 ›› 2015, Vol. 33 ›› Issue (1): 87-94.doi: 10.3969/j.issn.0255-8297.2015.01.010

利用通道间相关性的CFA图像盲取证

上海大学通信与信息工程学院，上海200444

收稿日期:2014-12-02 修回日期:2014-12-29 出版日期:2015-01-30 发布日期:2015-01-04
通信作者: 方针，博士，副教授，图像处理、数字媒体内容安全，E-mail: zhfang@staff.shu.edu.cn
基金资助:
国家自然科学基金（No.61472235）；教育部博士点基金（No.20113108110010）；上海市浦江人才计划基
金（No.13PJ1403200）；上海市东方学者专项

Forgery Detection via Inter-channel Correlation of CFA Images

School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Received:2014-12-02 Revised:2014-12-29 Online:2015-01-30 Published:2015-01-04

摘要/Abstract

摘要： 数码相机大多采用单传感器通过颜色滤波阵列（color filter array, CFA）插值获取彩色图像. 为此, 利用通道间CFA 插值引入的频谱相关性鉴别图像真实性. 分析插值图像与自然图像的频谱差异，基于绿-红分量频谱差的高频区域提取取证特征；对待鉴定图像重新插值，根据重插值前后的取证特征变化检测图像篡改. 实验结果表明，该方法可以定位图像篡改且对JPEG 压缩有较强的鲁棒性.

关键词: 篡改检测, 图像取证, CFA插值, 通道间相关性

Abstract: Most digital cameras use a single sensor to capture only one component among the three colors (RGB) for each pixel, together with a color filter array (CFA) interpolating the other two after data acquisition. In this work, spectral correlation introduced by inter-channel CFA interpolation is exploited to realize image authentication. By analyzing differences in the frequency spectrum between inter-channel interpolated images and the natural ones, we extract forensics features from the high frequency areas of the green-red spectral difference. After re-interpolating the test image, we detect tampered images according to changes in the forensic features. Experimental results demonstrate effectiveness in forgery detection and robustness to JPEG compression.

Key words: forgery detection, image forensics, CFA interpolation, inter-channel correlation

中图分类号:

TP391

张晓琳，方针，张新鹏. 利用通道间相关性的CFA图像盲取证[J]. 应用科学学报, 2015, 33(1): 87-94.

ZHANG Xiao-lin, FANG Zhen, ZHANG Xin-peng. Forgery Detection via Inter-channel Correlation of CFA Images[J]. Journal of Applied Sciences, 2015, 33(1): 87-94.

参考文献

[1] CAO H, KOT A C. Accurate detection of demosaicing regularityfor digital image forensics [J]. IEEE Transactions Information Forensics and Security, 2009, 4(4): 899-910.
[2] CHEN M , FRIDRICH J, GOL JAN M , LUKAS J. Determining image origin and integrit y using sensor noise [J]. IEEE Transactions on Information Security and Forensics, 2008, 3(1) : 74-90.
[3] HSU Y F, CHANG S F. Camera response functions for image forensics: an automatic algorithm for splicing detection[J]. IEEE Transactions on Information Forensics and Security, 2010, 5(4): 816-825.
[4] POPESCU A C, FARID H. Exposing digital forgeries by detecting traces of resampling[J]. IEEE Transactions on Signal Processing, 2005,53(2): 758-767.
[5] GALLAGHER A C. Detection of linear and cubic interpolation in jpeg compressed images [J]. Proceedings of the Second Canadian Conference on Computer and Robot Vision, USA, 2005: 65-72.
[6] GALLAGHER A C, CHEN T (2008) .Image authentication by detecting traces of demosaicing[]. CVPRW’08: 1-8.
[7] FERRARA P, BIANCHI T, de ROSA A. Image forgery localization via fine-grained analysis of cfa artifacts [J]. IEEE Transactions on Information Forensics and Security, 2012, 7 (5): 1566-1577.
[8] LI L, XUE J, WANG X. A robust approach to detect digital forgeries by exploring correlation patterns [J]. Pattern Analysis and Applications, 2013: 1-15.
[9] HO John S, AU Oscar C, ZHOU J, GUO Y. Inter-channel demosaicking traces for digital image forensics [C]//Procession of IEEE International Conference on Multimedia and Expo, Suntec City, 2010: 1475-1480.
[10] GUNTURK B K, ALTUNBASAK Y R, MERSEREAU M. Color plane interpolation using alternating projections [J]. IEEE Transactions on Image Processing, 2002,11( 9): 997-1013.
[11] HAMILTON J F, ADAMS J E. Adaptive color plane interpolation in single sensor color electronic camera, U.S. Patent 5 629 734, 1997.
[12] ZHANG L, WU X. Color demosaicking via directional linear minimum mean square-error estimation [J]. IEEE Transactions on Image Processing,2005, 14(12): 2167-2178.
[13] LIAN N X, ZAGORODNOV V, TAN Y P. Edge-preserving image denoising via optimal color space projection [J]. IEEE Transactions on Image Processing, 2006, 15(9): 2575-2587.
[14] CHUNG K H, CHAN Y H. Color demosaicing using variance of color differences [J]. IEEE Transactions Image Process, 2006, 15(10): 2944-2955.
[15] LIAN N X, CHANG L , TAN Y P. Adaptive filtering for color filter array demosaicking [J]. IEEE Transactions on Image Processing, 2007, 16 (10): 2515-2525
[16] PEKKUCUKSEN I, ALTUNBASAK Y. Multiscale gradients-based color filter array interpolation [J]. IEEE Transactions on Image Processing, 2013, 22 (1): 157-165.
[17] KIMMEL R. Demosaicing: image reconstruction from CCD samples [J]. IEEE Transactions on Image Processing,1999,8(11): 1221-1228.
[18] SCHAEFER G, STICH M. UCID - an uncompressed colour image database process [C]// SPIE, Storage and Retrieval Methods and Applications for Multimedia 2004, San Jose, USA: 472-480.

利用通道间相关性的CFA图像盲取证

Forgery Detection via Inter-channel Correlation of CFA Images

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