一种基于差分DCT系数对直方图的图像取证方法

doi:10.3969/j.issn.0372-2112.2016.01.002

电子学报 ›› 2016, Vol. 44 ›› Issue (1): 8-13.DOI: 10.3969/j.issn.0372-2112.2016.01.002

一种基于差分DCT系数对直方图的图像取证方法

杨富圣, 高铁杠

南开大学软件学院, 天津 300071

收稿日期:2014-05-28 修回日期:2015-06-24 出版日期:2016-01-25
- 作者简介:
- 杨富圣 男,1984年生于天津.博士研究生,研究方向为多媒体信息安全技术. E-mail:yong_nk@sina.com 高铁杠 男,1966年生于河北河间.教授,博士生导师,研究方向为数字媒体安全技术、软件工程.
- 基金资助:
- 天津市自然科学基金重点项目 (No.11JCZDJC16000)

An Image Forensic Algorithm Based on Differential Coefficient-Pair Histogram in DCT Domain

YANG Fu-sheng, GAO Tie-gang

College of Software, Nankai University, Tianjin 300071, China

Received:2014-05-28 Revised:2015-06-24 Online:2016-01-25 Published:2016-01-25
- Supported by:
- Key Program of National Natural Science Foundation of Tianjin Municipality, China (No.11JCZDJC16000)

摘要/Abstract

摘要：

提出了一种基于差分DCT域系数对直方图的图像拼接篡改检测方法.该方法首先对图像进行DCT变换,而后分别计算DCT系数矩阵的水平、垂直、主对角线、副对角线四个方向的差分DCT系数矩阵,并对得到的差分DCT系数矩阵进行系数对直方图化,提取特征向量.最后,利用支持向量机对真实图像和篡改后的图像进行分类识别.实验结果表明,在相关的测试数据集上,和现存的一些算法相比,该方法不仅具有较低的计算复杂度,同时,其检测性能在目前所有提出的算法中达到最高,性能优良.

关键词: 图像取证, 差分DCT系数, 系数对直方图, 支持向量机

Abstract:

An image forensic method based on coefficient-pair histogram of differential DCT coefficient was proposed.In the method, the image is firstly transformed by DCT, and then the differential DCT coefficient of four directions, such as horizontal direction, vertical direction, diagonal direction and the second diagonal direction are computed.After that, the coefficient-pair histogram for each differential DCT coefficient is calculated.Finally, support vector machine (SVM) is used to classify the authentic and spliced image through training the feature vectors of authentic and that of tampered image.The experimental results show that the proposed approach not only has low computing complexity, but also outperforms all the state-of-the-art methods in detection rate on the same test database.

Key words: image forensics, differential DCT coefficient, coefficient-pair histogram, support vector machine

中图分类号:

TN911.7

杨富圣, 高铁杠. 一种基于差分DCT系数对直方图的图像取证方法[J]. 电子学报, 2016, 44(1): 8-13.

YANG Fu-sheng, GAO Tie-gang . An Image Forensic Algorithm Based on Differential Coefficient-Pair Histogram in DCT Domain[J]. Acta Electronica Sinica, 2016, 44(1): 8-13.

参考文献

[1] Popescu A C, Farid H.Exposing Digital Forgeries by Detecting Duplicated Image Regions[R].Hanover:Dartmouth College Department of Computer Science Technical, 2004, TR2004-515.
[2] Amerini I, Ballan L, Caldelli R, et al.A sift-based forensic method for copy-move attack detection and transformation recovery[J].IEEE Transactions on Information Forensics and Security, 2011, 6(3):1099-1110.
[3] Pan X, Lyu S.Region duplication detection using image feature matching[J].IEEE Transactions on Information Forensics and Security, 2010, 5(4):857-867.
[4] Wu Q, Wang S, Zhang X.Log-polar based scheme for revealing duplicated regions in digital images[J].IEEE Signal Processing Letters, 2011, 18(10):559-562.
[5] Avidan S, Shamir A.Seam carving for content-aware image resizing[J].ACM Transactions on Graphics, 2007, 26(3):10.
[6] 王波, 孙璐璐, 孔祥维, 等.图像伪造中模糊操作的异常色调率取证技术[J].电子学报, 2006, 34(12A):2451-2454. Wang Bo, Sun Lu-lu, Kong Xiang-wei, et al.Image forensics technology using abnormity of local hue for blur detection[J].Acta Electronica Sinica, 2006, 34(12A):2451-2454.(in Chinese)
[7] 周琳娜, 王东明, 郭云彪, 等.基于数字图像边缘特性的形态学滤波取证技术[J].电子学报, 2008, 36(6):1047-1051. Zhou Lin-na, Wang Dong-ming, Guo Yun-biao, et al.Exposing digital forgeries by detecting image blurred mathematical morphology edge[J].Acta Electronica Sinica, 2008, 36(6):1047-1051.(in Chinese)
[8] Cao G, Zhao Y, Ni R, et al.Contrast enhancement-based forensics in digital images[J].IEEE Transactions on Information Forensics and Security, 2014, 9(3):515-525.
[9] Luo W, Huang J, Qiu G.JPEG error analysis and its applications to digital image forensics[J].IEEE Transactions on Information Forensics and Security, 2010, 5(3):480-491.
[10] Farid H.Detecting Digital Forgeries Using Bispectral Analysis[R]. Cambridge:Massachusetts Institute of technology, 1999, AIM-1657.
[11] Ng T T, Chang S F, Sun Q.A Data Set of Authentic and Spliced Image Blocks[R].Columbia:Columbia University, ADVENT Technical, 2004.203.
[12] Ng T T, Chang S F.Blinddetection of digital photomontage using higher order statistics[DB/OL].http://www.ee.columbia.edu/dvmm/, 2004-01.
[13] Fu D, Shi Y Q, Su W.Detection of image splicing based on hilbert-huang transform and moments of characteristic functions with wavelet decomposition[A].Proceedings of the 5th International Workshop on Digital Watermarking[C].Berlin:Springer, 2006.177-187.
[14] Chen W, Shi Y Q, Su W.Imagesplicing detection using 2-D phase congruency and statistical moments of characteristic function[A].Proceedings of SPIE Electronic Imaging[C].California:SPIE, 2007.65050R-1.
[15] Shi Y Q, Chen C, Chen W.A natural image model approach to splicing detection[A].Proceedings of the 9th Workshop on Multimedia & Security[C].New York:ACM, 2007.51-62.
[16] Sutthiwan P, Shi Y Q, Dong J, et al.New developments in color image tampering detection[A].Proceedings of IEEE International Symposium on Circuits and Systems[C].New York:IEEE, 2010.3064-3067.
[17] He Z, Lu W, Sun W, et al.Digital image splicing detection based on Markov features in DCT and DWT domain[J].Pattern Recognition, 2012, 45(12):4292-4299.
[18] Saleh S Q, Hussain M, Muhammad G, et al.Evaluation ofimage forgery detection using multi-scale weber local descriptors[A].Proceedings of Advances in Visual Computing[C].Berlin:Springer, 2013.416-424.
[19] Shi Y Q, Chen C, Chen W.A markov process based approach to effective attacking JPEG steganography[A].Proceedings of the 8th Information Hiding Workshop[C].Berlin:Springer, 2006.249-264.
[20] Qian-Lan D.The blind detection of information hiding in color image[A].Proceedings of the 2nd International Conference on Computer Engineering and Technology[C].New York:IEEE, 2010.V7-346-V7-348.
[21] Shabanifard M, Shayesteh M G, Akhaee M A.Forensic detection of image manipulation using the Zernike moments and pixel-pair histogram[J].IET Image Processing, 2013, 7(9):817-828.
[22] Jing Dong.CASIA tampered image detection evaluation database[DB/OL].http://forensics.idealtest.org, 2011.
[23] Chang C C, Lin C J.LIBSVM:a library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):1-25.
[24] Qiu X, Li H, Luo W, et al.A universal image forensic strategy based on steganalytic model[A].Proceedings of the 2nd ACM Workshop on Information Hiding and Multimedia Security[C].New York:ACM, 2014.165-170.
[25] ARocha, A Piva, J Huang.Images corpus of the 1st IEEE IFS-TC image forensics challenge[DB/OL].http://ifc.recod.ic.unicamp.br/fc.website/index.py?sec=5, 2013-06.

一种基于差分DCT系数对直方图的图像取证方法

An Image Forensic Algorithm Based on Differential Coefficient-Pair Histogram in DCT Domain

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	曹源, 宋迪, 胡小溪, 孙永奎. 基于改进时域多尺度散布熵与支持向量机的转辙机故障诊断[J]. 电子学报, 2023, 51(1): 117-127.
[2]	王波, 王悦, 王伟, 侯嘉尧. 开放环境下自适应聚类优化包络的相机来源取证[J]. 电子学报, 2022, 50(8): 1840-1850.
[3]	吴国庆, 王罗胜斌, 庞晨, 李永祯, 王雪松. 雷达极化域变焦角反组合体对抗方法：抗冲淡式干扰[J]. 电子学报, 2022, 50(12): 2969-2983.
[4]	孙晨峰, 吕卫民, 戴洪德, 张浩晨. 一种基于TimeGAN和OCSVM的多元退化设备小子样数据增广方法[J]. 电子学报, 2022, 50(11): 2678-2687.
[5]	杨国为, 万鸣华, 赖志辉, 张凡龙, 黄伟强. 具有合适拒识机制的高正确识别率分类器设计[J]. 电子学报, 2021, 49(8): 1569-1576.
[6]	罗忠涛, 夏杭, 卢琨, 何子述. 超视距雷达中距离-多普勒图的瞬态干扰自动识别方法[J]. 电子学报, 2021, 49(7): 1279-1285.
[7]	钱忠胜, 俞情媛, 宋涛, 朱懿敏, 祝洁, 赵畅. 基于支持向量机回归模型的测试用例生成与重用[J]. 电子学报, 2021, 49(7): 1386-1391.
[8]	张芳鑫, 王法松, 李睿, 左婷. GSSK‑VLC系统中基于SVM的LED选择算法[J]. 电子学报, 2021, 49(7): 1400-1405.
[9]	刘芳, 韩笑. 基于小波变换和深度网络的着陆地貌图像分类[J]. 电子学报, 2021, 49(11): 2171-2176.
[10]	史聪伟, 赵杰煜, 陈瑜. 利用网格卷积特征的三维形变目标分类[J]. 电子学报, 2020, 48(4): 648-653.
[11]	张学军, 景鹏, 何涛, 孙知信. 基于变分模态分解的癫痫脑电信号分类方法[J]. 电子学报, 2020, 48(12): 2469-2475.
[12]	方佳艳, 刘峤. 具有同步化特征选择的迭代紧凑非平行支持向量聚类算法[J]. 电子学报, 2020, 48(1): 44-58.
[13]	周国华, 卢剑炜, 顾晓清, 殷新春. 基于简约凸壳的一类模糊支持向量机[J]. 电子学报, 2019, 47(8): 1708-1716.
[14]	郑威迪, 李志刚, 贾涵中, 高闯. 基于改进型鲸鱼优化算法和最小二乘支持向量机的炼钢终点预测模型研究[J]. 电子学报, 2019, 47(3): 700-706.
[15]	刘颖, 胡丹, 范九伦, 王富平, 李大湘. 基于融合特征的现勘图像检索结果优化算法[J]. 电子学报, 2019, 47(2): 296-301.