Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Jun-Liu Zhong"'
Publikováno v:
Soft Computing. 26:11461-11478
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0042da5d385514c7fc6cbe5751376fb0
https://doi.org/10.1007/s00500-022-07432-x
https://doi.org/10.1007/s00500-022-07432-x
Publikováno v:
International Journal of Pattern Recognition and Artificial Intelligence. 36
The image copy–move forgery can achieve the realistic effects without leaving any obvious manipulation traces because the copied source and pasted target belong to the homogenous sources. Image copy–move forgery detection (CMFD) is still a seriou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a6c54985349e76da3d0d6c06a1be190a
https://doi.org/10.1142/s0218001422540192
https://doi.org/10.1142/s0218001422540192
Publikováno v:
International Journal of Pattern Recognition and Artificial Intelligence. 36
Currently, image copy–move forgery issues have brought increasingly serious problems to the social fields. Many research have been devoted to address the image copy–move forgery issues. However, image copy–move forgery detection (CMFD) is still
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::077a7fed2bb0360ccdc35a5e916cc468
https://doi.org/10.1142/s0218001422540167
https://doi.org/10.1142/s0218001422540167
Publikováno v:
Information Sciences. 537:184-202
This paper proposes a video copy-move forgery detection method to effectively address inter/intra-frame forgeries both at the frame and pixel level. First, a unified moment framework is proposed to extract multi-dimensional dense moment features from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a96aebe878e36627c81594663796c0c
https://doi.org/10.1016/j.ins.2020.05.134
https://doi.org/10.1016/j.ins.2020.05.134
Autor:
Jun-Liu Zhong, Chi-Man Pun
Publikováno v:
Information Sciences. 512:675-692
In this paper, we propose a two-pass hashing feature representation and searching method for copy-move forgery detection with a good score and high efficiency. First, the normalized moment transformation is presented to extract the corresponding bloc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::daa0f9390303d3c05cbdbd734affc165
https://doi.org/10.1016/j.ins.2019.09.085
https://doi.org/10.1016/j.ins.2019.09.085
Autor:
Jun-Liu Zhong, Chi-Man Pun
Publikováno v:
IEEE Transactions on Information Forensics and Security. 15:2134-2146
A novel image copy-move forgery detection scheme using a Dense-InceptionNet is proposed in this paper. Dense-InceptionNet is an end-to-end, multi-dimensional dense-feature connection, Deep Neural Network (DNN). It is the first DNN model to autonomous
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d707290ebba6b13acfecb91e5dfd4d32
https://doi.org/10.1109/tifs.2019.2957693
https://doi.org/10.1109/tifs.2019.2957693
Autor:
Jun-Liu Zhong, Chi-Man Pun
Publikováno v:
Multimedia Tools and Applications. 78:26313-26339
Copy-move forgery detection can generally be divided into two categories: block-based or keypoint-based methods. However, the existing block-based methods are usually lack of efficiency and the keypoint-based methods have not good detection performan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9386a09fae94ec2e9387b2f25da94af4
https://doi.org/10.1007/s11042-019-07817-5
https://doi.org/10.1007/s11042-019-07817-5
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Yan Fen Gan, Jun Liu Zhong
Publikováno v:
Advanced Materials Research. :630-633
Based on analyzing the mathematical model of Brushless DC Motor (BLDCM) and the key researching on electric process state spaces of BLDCM main module, taking its one state space as an example, BLDCM system is proposed as a new method of simulation mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::87abb72289d168e1a5b8a84386b245b5
https://doi.org/10.4028/www.scientific.net/amr.1049-1050.630
https://doi.org/10.4028/www.scientific.net/amr.1049-1050.630