基于扩展马尔科夫特征的Seam-Carving篡改检测

doi:10.3969/j.issn.1000-436x.2014.06.006

Abstract

Abstract:

To deal with the digital image forgery using Seam-Carving, a detection algorithm based on expanded Markov features was proposed. The algorithm takes full acount of the image frequency domain change caused by Seam-Carving operation, merges the features based on traditional and expands Markov transfer-probability matrix. Trained by SVM, the merged Markov feature can identify the Seam-Carving forgery more effectively. The experiment result shows that the performance of the proposed method is better than that of the method based on traditional Markov features and other existing methods.

Key words: image forensics; image forgery, Seam-Carving, Markov feature, image resizing

Guo-rui SHENG,Tie-gang GAO,Li FAN,Lin GAO,Fu-sheng YANG,Shun ZHANG. Seam-Carving forgery detection based on expanded Markov features[J]. Journal on Communications, 2014, 35(6): 39-46.

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References 15

[1]	REY C , DUGELAY J L . A survey of watermarking algorithms for image authentication[J]. EURASIP Journal on Applied Signal Proc-essing, 2002,(1): 613-621.
[2]	POTDAR V M , HAN S , CHANG E . A survey of digital image water-marking techniques[A]. 2005 3rd IEEE International Conference on Industrial Informatics, INDIN'05[C]. Perth, WA, Australia, 2005.709-716.
[3]	张家树，田蕾 . 一种新的基于密钥的混沌数字水印方法[J]. 通信学报， 2004,25(8): 96-101. ZHANG J S , TIAN L . A new chaotic digital watermarking method based on private key[J]. Journal on Communications, 2004,25(8): 96-101.
[4]	和红杰，张家树 . 基于混沌置乱的分块自嵌入水印算法[J]. 通信学报， 2006,27(7): 80-86. HE H J , ZHANG J S . Chaos-based scramble self-embedding water-marking algorithm[J]. JJournal on Communications, 2006,27(7): 80-86.
[5]	CAO Y J , GAO T G , FAN L . A robust detection algorithm for copy-move forgery in digital images[J]. Forensic Science International, 2012,214(1-3): 33-43.
[6]	PAN X , LYU S . Region duplication detection using image feature matching[J]. Information Forensics and Security, IEEE Transactions on, 2010,5(4): 857-867.
[7]	NG T T , CHANGS F . Blind detection of photomontage using higher order statistics[A]. Proceedings of the 2004 International Symposium on Circuits and Systems, ISCAS'04[C]. IEEE Vancouver, Canada, 2004. 688-691.
[8]	NG T T , CHANG S F . A model for image splicing[A]. Proceedings of ACM Sigcomm ’98[C]. Singapore, 2004.1169-1172.
[9]	AVIDAN S , SHAMIR A . Seam carving for content-aware image resizing[J]. ACM Transactions on Graphics (TOG), 2007,26(3): 10.
[10]	LU W , WU M . Seam carving estimation using forensic hash[A]. Pro-ceedings of the Thirteenth ACM Multimedia Workshop on Multimedia and Security[C]. New York, USA, 1998.9-14.
[11]	FILLION C , SHARMA G . Detecting content adaptive scaling of im-ages for forensic applications[A]. IS＆T/SPIE Electronic Imaging, Proc SPIE 7541, Media Forensics and Security II[C]. San Jose, Cali-fornia, 2010.75410Z-12.
[12]	SARKAR A , NATARAJ L , MANJUNATH B S . Detection of seam carving and localization of seam insertions in digital images[A]. Pro-ceedings of the 11th ACM Workshop on Multimedia and Security[C]. ACM, New York, USA, 2009.107-116.
[13]	CORTES C , VAPNIK V . Support-vector networks[J]. Machine Learn-ing, 1995,20 (3): 273-297.
[14]	NG T T , CHANG S F . A Data Set of Authentic and Spliced Image Blocks[R]. Technical Report 203-2004-3, Columbia University, 2004.
[15]	CHANG C C , LIN C J . LIBSVM:a library for support vector ma-chines[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2011,2(3): 27.

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图像	TPR/%	TNR/%	Accuracy/%
Au-S	90	84	87
Au-SS-H	84	78	81
Au-SS-O	100	84	92
Au-SS-V	85	92	88
Au-T	86	82	84
Au-TS-H	94	90	92
Au-TS-O	84	86	85
Au-TS-V	90	94	92
Au-TT-H	92	94	93
Au-TT-O	92	98	95
Au-TT-V	92	94	93
All	90	88.6	89.3

图像	TPR/%	TNR/%	Accuracy/%
Au-S	74	92	83
Au-SS-H	76	66	71
Au-SS-O	90	70	80
Au-SS-V	90	82	86
Au-T	96	82	89
Au-TS-H	88	80	84
Au-TS-O	92	68	80
Au-TS-V	92	66	79
Au-TT-H	94	76	85
Au-TT-O	94	96	95
Au-TT-V	83	91	87
All	88	79	83.5

算法	TPR/%	TNR/%	Accuracy/%
Sarkar	88	79	83.5
Fillion	91	80.4	85.7
本文算法	90	88.6	89.3

Seam-Carving forgery detection based on expanded Markov features

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References 15

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