一種基于逆序廣義2近鄰的圖像多重復(fù)制粘貼篡改檢測(cè)算法

李巖; 劉念; 張斌; 袁開(kāi)國(guó); 楊義先

doi:10.11999/JEIT141271

一種基于逆序廣義2近鄰的圖像多重復(fù)制粘貼篡改檢測(cè)算法

doi: 10.11999/JEIT141271

1.
(北京郵電大學(xué)信息安全中心北京 100876) ②(北京電子科技學(xué)院北京 100070) ③(中國(guó)信息安全認(rèn)證中心北京 100020)

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61170271, 61121061)，新聞出版署項(xiàng)目(GXTC-CZ-1015004/15-1)和中央高?；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(BUPT2012RC0217)資助課題

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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2014-09-30
- 修回日期: 2015-04-03
- 刊出日期: 2015-07-19

Image Multiple Copy-move Forgery Detection Algorithm Based on Reversed-generalized 2 Nearest-neighbor

1.
(Information Security Center, Beijing University of Posts and Telecommunications, Beijing 100876, China)
2.
(Beijing Electronic Science and Technology Institute, Beijing 100070, China)

摘要

摘要: 為了解決數(shù)字圖像多重復(fù)制粘貼篡改檢測(cè)問(wèn)題，克服廣義2近鄰(g2NN)算法對(duì)匹配特征點(diǎn)漏檢的缺點(diǎn)，該文提出逆序廣義2近鄰(Rg2NN)算法。在計(jì)算匹配特征點(diǎn)時(shí)，該算法采用逆序方式計(jì)算特征點(diǎn)之間的匹配關(guān)系，可以更加準(zhǔn)確地計(jì)算出所有與待檢測(cè)特征點(diǎn)相匹配的特征點(diǎn)。實(shí)驗(yàn)證明，Rg2NN算法比g2NN算法計(jì)算出來(lái)的匹配特征點(diǎn)更加準(zhǔn)確，提高了g2NN算法對(duì)多重復(fù)制粘貼篡改圖像的檢測(cè)能力，當(dāng)圖像中的一塊區(qū)域被復(fù)制后在多處粘貼，或多塊區(qū)域被復(fù)制粘貼時(shí)可以準(zhǔn)確計(jì)算出復(fù)制粘貼區(qū)域。
- 圖像處理 /
- 圖像取證 /
- 復(fù)制粘貼 /
- 特征點(diǎn)
Abstract: For the consideration of the multiple copy-move forgery detection of digital images, and to avoid missing the matching feature points when generalized 2 Nearest-Neighbor (g2NN) algorithm is applied, Reversed generalized 2 Nearest-Neighbor (Rg2NN) algorithm is proposed. Reverse order is used in feature points matching, so that all feature points that match with the detected point can be calculated accurately. The experiment results show that the matching feature points calculated by Rg2NN are more accurate than by g2NN, and the ability of g2NN in detecting multiple copy-move forgery is improved. When one patch in the image is copied and pasted multiple times or two or more patches are copied and pasted, the copy-move map can be localized precisely by the Rg2NN algorithm.
- Image processing /
- Image forensics /
- Copy-move /
- Feature point

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參考文獻(xiàn)(24)

Qazi T, Hayat K, Khan S U, et al.. Survey on blind image forgery detection[J]. IET Image Processing, 2013, 7(7): 660-670.

Al-Qershi O M and Khoo B E. Passive detection of copy- move forgery in digital images: state-of-the-art[J]. Forensic Science International, 2013, 231(1/3): 284-295.

Ali Qureshi M and Deriche M. A review on copy move image forgery detection techniques[C]. Proceedings of the 11th International Multi-Conference on Systems, Signals Devices (SSD), Barcelona, Spain, 2014: 1-5.

王青, 張榮. 基于DCT系數(shù)雙量化映射關(guān)系的圖像盲取證算法[J]. 電子與信息學(xué)報(bào), 2014, 36(9): 2068-2074.

Wang Qing and Zhang Rong. Exposing digital image forgeries based on double quantization mapping relation of DCT coefficient[J]. Journal of Electronics Information Technology, 2014, 36(9): 2068-2074.

Wu Y, Deng Y, Duan H, et al.. Dual tree complex wavelet transform approach to copy-rotate-move forgery detection[J]. SCIENCE CHINA Information Sciences, 2014, 57(1): 1-12.

Wang W, Dong J, and Tan T N. Exploring DCT coefficient quantization effects for local tampering detection[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(10): 1653-1666.

Niu S Z, Meng X Z, and Cui H L. Digital image forensics using orthogonal 1-D objects[J]. Chinese Journal of Electronics, 2014, 23(3): 545-549.

Liu B, Pun C M, and Yuan X C. Digital image forgery detection using JPEG features and local noise discrepancies [J]. The Scientific World Journal, 2014(1): 1-12.

Fridrich A J, Soukal B D, and Luk? A J. Detection of copy-move forgery in digital images[C]. Proceedings of the Digital Forensic Research Workshop, Cleveland, USA, 2003: 55-61.

Popescu A C and Farid H. Exposing digital forgeries by detecting duplicated image regions[R]. Department of Computer Science, Dartmouth College, 2004.

Jaberi M, Bebis G, Hussain M, et al.. Accurate and robust localization of duplicated region in copy-move image forgery [J]. Machine Vision and Applications, 2014, 25(2): 451-475.

Bo X, Junwen W, Guangjie L, et al.. Image copy-move forgery detection based on SURF[C]. Proceedings of the International Conference on Multimedia Information Networking and Security (MINES), Nanjing, China, 2010: 889-892.

Mishra P, Mishra N, Sharma S, et al.. Region duplication forgery detection technique based on SURF and HAC[J]. The Scientific World Journal, 2013(1): 1-8.

Chen L, Lu W, Ni J, et al.. Region duplication detection based on Harris corner points and step sector statistics[J]. Journal of Visual Communication and Image Representation, 2013, 24(3): 244-254.

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.

Bay H, Ess A, Tuytelaars T, et al.. Speeded-up robust features (SURF)[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.

Beis J S and Lowe D G. Shape indexing using approximate nearest-neighbour search in high-dimensional spaces[C]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, 1997: 1000-1006.

Amerini I, Ballan L, Caldelli R, et al.. Copy-move forgery detection and localization by means of robust clustering with J-linkage[J]. Signal Processing: Image Communication, 2013, 28(6): 659-669.

Kakar P and Sudha N. Exposing postprocessed copy-paste forgeries through transform-invariant features[J]. IEEE Transactions on Information Forensics and Security, 2012, 7(3): 1018-1028.

Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

Toldo R and Fusiello A. Robust multiple structures estimation with J-linkage[C]. Proceedings of the 10th European Conference on Computer Vision, Marseille, France, 2008, 5302: 537-547.

Suzuki S. Topological structural analysis of digitized binary images by border following[J]. Computer Vision, Graphics, and Image Processing, 1985, 30(1): 32-46.

Jegou H, Douze M, and Schmid C. Hamming embedding and weak geometric consistency for large scale image search[C]. Proceedings of the 10th European Conference on Computer Vision, Marseille, France, 2008, 5302: 304-317.

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