基于超像素和游程直方图的对比度修改检测算法

doi:10.11999/JEIT160161

Abstract
Figure/Table
References (22)
Related Citation (15)

Download: PDF (11757 KB)
Export: BibTeX | EndNote (RIS)

Abstract

A novel image forensic algorithm against contrast modification based on superpixel and histogram of run length is proposed. In the proposed algorithm, images are firstly divided by superpixel, then run length histogram features of each block are extracted, and those of different orientation are subsequently merged. After normalization of the prior features, the leaps in the histogram are calculated numerically. Lastly, the generated features of blocks are trained by Support Vector Machin (SVM) classifier. Large amounts of experiments show that, the proposed algorithm has low cost of computation complexity. And compared with some state-of-the-art algorithms, it has better performance with many test databases. Furthermore, the proposed algorithm can not only located the tempered area, but also can exactly describe the shape of tempered area.

Key words： Image processing Contrast modification Superpixel Run length histogram Leaps in the histogram numerically Support Vector Machin (SVM)

Received: 19 February 2016 Published: 30 September 2016

PACS:

TN911.73

Fund:

Tianjin Natural Science Foundation (16JCYBJC 15700)

Corresponding Authors: YANG Liang E-mail: yangliang.nkcs@gmail.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	GAO Tiegang
	YANG Liang
	XUAN Yan
	TONG Jing

Cite this article:

GAO Tiegang,YANG Liang,XUAN Yan等. Contrast Modification Forensic Algorithm Based on Superpixel and Histogram of Run Length[J]. JEIT, 2016, 38(11): 2787-2794.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160161 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I11/2787

[1]	骆伟祺, 黄继武, 丘国平. 鲁棒的区域复制图像篡改检测技术[J]. 计算机学报, 2007, 30(11): 1998-2007.
	LUO Weiqi, HUANG Jiwu, and QIU Guoping. Robust detection of region-duplication forgery in digital image[J]. Chinese Journal of Computers, 2007, 30(11): 1998-2007.
[2]	李晓飞, 申铉京, 陈海鹏, 等. 基于数字签名方式的图像真伪鉴别算法[J]. 计算机研究与发展, 2012, 49(6): 1348-1356.
	LI Xiaofei, SHEN Xuanjing, CHEN Haipeng, et al. An image identification algorithm based on digital signature method[J]. Computer Research and Development, 2012, 49(6): 1348-1356.
[3]	ANDALIBI M and CHANDLER D. Digital image watermarking via adaptive logo texturization[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5060-5073.
[4]	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.
[5]	ARICI T, DIKBAS S, and ALTUNBASAK Y. A histogram modification framework and its application for image contrast enhancement[J]. IEEE Transactions on Image Processing, 2009, 18(9): 1921-1935.
[6]	CAO G, ZHAO Y, NI R, et al. Anti-forensics of contrast enhancement in digital images[C]. 12th ACM Workshop on Multimedia and Security, ACM, Rome, Italy, 2010: 25-34.
[7]	STAMM M C and LIU K J R. Forensic detection of image manipulation using statistical intrinsic fingerprints[J]. IEEE Transactions on Information Forensics and Security, 2010, 5(3): 492-506.
[8]	DE ALESSIA Rosa, FONTANI Marco, MASSAI Matteo, et al. Second-order statistics analysis to cope with contrast enhancement counter-forensics[J]. IEEE Signal Processing Letters, 2015, 22(8): 1132-1136.
[9]	LIN X, LI C, and HU Y. Exposing image forgery through the detection of contrast enhancement[C]. 2013 20th IEEE International Conference on Image Processing (ICIP), Melbourne Australia, 2013: 4467-4471.
[10]	CAO G, ZHAO Y, NI R, et al. Attacking contrast enhancement forensics in digital images[J]. Science China Information Sciences, 2014, 57(5): 1-13.
[11]	SHEN J, DU Y, WANG W, et al. Lazy random walks for superpixel segmentation[J]. IEEE Transactions on Image Processing, 2014, 23(4): 1451-1462.
[12]	RADHAKRISHNA A, APPU S, KEVIN S, et al. SLIC superpixels compared to state-of-the-Art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.
[13]	GOLOMB S W. Run-length encodings[J]. IEEE Transactions on Information Theory, 1966, 12(3): 317-319.
[14]	TANG X. Texture information in run-length matrices[J]. IEEE Transactions on Image Processing, 1998, 7(11): 1602-1609.
[15]	STAMM M C and LIU K J R. Forensic estimation and reconstruction of a contrast enhancement mapping[C]. IEEE International Conference on Acoustics Speech & Signal Processing, Dallas, TX, USA 2010, 23(3): 1698-1701.
[16]	YANG Liang, GAO Tiegang, XUAN Yan, et al. Contrast modification forensics algorithm based on merged weight histogram of run length[J]. International Journal of Digital Crime and Forensics, 2016, 8(2): 27-35.
[17]	SHALEV S S and SREBRO N. SVM optimization: inverse dependence on training set size[C]. Proceedings of the 25th International Conference on Machine Learning, ACM, Helsinki, Finland, 2008: 928-935.
[18]	SCHAEFER G and STICH M. UCID - An uncompressed color image database[C]. Storage & Retrieval Methods & Applications for Multimedia 2004, San Jose, CA, USA, 2003, 5307: 472-480.
[19]	CASIA ITDE Database. http://forensics.idealtest.org. 2016.
[20]	IEEE IFS-TC image forensics challenge. http://ifc.recod. ic.unicamp.br/fc.website/index.py?sec=0. 2016.
[21]	CHANG C and LIN C. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems & Technology, 2001, 2(3): 389-396.
[22]	FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8): 861-874.