TY - GEN
T1 - Passive Image Copy–Move Forgery Detection Based on ORB Features
AU - Xue, Zhao
AU - Tian, Lihua
AU - Li, Chen
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - Digital image acquisition is extremely easy nowadays, but this convenience comes with the illegal purposes of criminals. Therefore, it is very necessary to identify the authenticity of the image. This paper proposes an adaptive image copy–move forgery detection method based on ORB features. Firstly, the proposed method performs an adaptive image super-pixel segmentation. Secondly, adaptive image sampling is carried out for each super-pixel block according to the block size. Third, quad-tree is used to store the key-points extracted by oFAST, and then the extracted key-points are described in rBRIEF. After that, Brute Force Matcher and hamming distance are adopted in the proposed method to perform key-points matching. Finally, MorphSnake algorithm is employed to locate the forged areas. Experiments have shown that compared with the current advanced detection methods, this one has stronger robustness to the attack of brightness, color, and contrast, and shorter running time.
AB - Digital image acquisition is extremely easy nowadays, but this convenience comes with the illegal purposes of criminals. Therefore, it is very necessary to identify the authenticity of the image. This paper proposes an adaptive image copy–move forgery detection method based on ORB features. Firstly, the proposed method performs an adaptive image super-pixel segmentation. Secondly, adaptive image sampling is carried out for each super-pixel block according to the block size. Third, quad-tree is used to store the key-points extracted by oFAST, and then the extracted key-points are described in rBRIEF. After that, Brute Force Matcher and hamming distance are adopted in the proposed method to perform key-points matching. Finally, MorphSnake algorithm is employed to locate the forged areas. Experiments have shown that compared with the current advanced detection methods, this one has stronger robustness to the attack of brightness, color, and contrast, and shorter running time.
KW - Copy–move tampering
KW - ORB
KW - SLIC
KW - oFAST
KW - rBRIEF
UR - https://www.scopus.com/pages/publications/85097275026
U2 - 10.1007/978-981-15-5887-0_45
DO - 10.1007/978-981-15-5887-0_45
M3 - 会议稿件
AN - SCOPUS:85097275026
SN - 9789811558863
T3 - Advances in Intelligent Systems and Computing
SP - 312
EP - 317
BT - Recent Developments in Intelligent Computing, Communication and Devices - Proceedings of ICCD 2019
A2 - WU, C. H.
A2 - PATNAIK, Srikanta
A2 - POPENTIU VLÃDICESCU, Florin
A2 - NAKAMATSU, Kazumi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Intelligent Computing, Communication and Devices, ICCD 2019
Y2 - 22 November 2019 through 24 November 2019
ER -