scholarly journals Secure Duplication Detection in Cloud using Chunk Based Technique

2019 ◽  
Vol 7 (6) ◽  
pp. 402-409
Author(s):  
Pranil Bari
Keyword(s):  
Duplication Detection

Apply Language Nature Rhythm to Large Scale Duplicated Text Detection

2012 ◽  
Vol 457-458 ◽  
pp. 635-640
Author(s):  
Fan Chen ◽  
Zhi Yong Feng ◽  
Geng Zhao
Keyword(s):  
Large Scale ◽  
High Accuracy ◽  
Text Detection ◽  
The Other ◽  
Duplication Detection ◽  
The Web

It is urgent that detect the duplication in large scale text in the Web. An arithmetic based on language rhythm for text duplication detection is proposed here. Get the nature rhythm marked by punctuations in text and build the rhythm compare matrix to complete the publication detection for each paragraph. This arithmetic is different with the other one which is based on words analysis. And it has a high accuracy and a low complicacy.

scholarly journals Genomon ITDetector: a tool for somatic internal tandem duplication detection from cancer genome sequencing data

2014 ◽  
Vol 31 (1) ◽  
pp. 116-118 ◽  
Author(s):  
Kenichi Chiba ◽  
Yuichi Shiraishi ◽  
Yasunobu Nagata ◽  
Kenichi Yoshida ◽  
Seiya Imoto ◽  
...  
Keyword(s):  
Genome Sequencing ◽  
Tandem Duplication ◽  
Cancer Genome ◽  
Sequencing Data ◽  
Internal Tandem Duplication ◽  
Cancer Genome Sequencing ◽  
Duplication Detection

Region duplication detection based on image segmentation and keypoint contexts

2017 ◽  
Vol 77 (11) ◽  
pp. 14241-14258 ◽  
Author(s):  
Cong Lin ◽  
Wei Lu ◽  
Wei Sun ◽  
Jinhua Zeng ◽  
Tianhua Xu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Region Duplication ◽  
Region Duplication Detection ◽  
Duplication Detection

Enhancements for duplication detection in bug reports with manifold correlation features

2016 ◽  
Vol 121 ◽  
pp. 223-233 ◽  
Author(s):  
Meng-Jie Lin ◽  
Cheng-Zen Yang ◽  
Chao-Yuan Lee ◽  
Chun-Chang Chen
Keyword(s):  
Bug Reports ◽  
Duplication Detection

scholarly journals An Improved Fingerprinting Algorithm for Detection of Video Frame Duplication Forgery

2012 ◽  
Vol 4 (3) ◽  
pp. 20-32 ◽  
Author(s):  
Yongjian Hu ◽  
Chang-Tsun Li ◽  
Yufei Wang ◽  
Bei-bei Liu
Keyword(s):  
Computational Efficiency ◽  
State Of The Art ◽  
Experimental Results ◽  
Video Frame ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
Dct Coefficients ◽  
Duplication Detection ◽  
Frame Duplication

Frame duplication is a common way of digital video forgeries. State-of-the-art approaches of duplication detection usually suffer from heavy computational load. In this paper, the authors propose a new algorithm to detect duplicated frames based on video sub-sequence fingerprints. The fingerprints employed are extracted from the DCT coefficients of the temporally informative representative images (TIRIs) of the sub-sequences. Compared with other similar algorithms, this study focuses on improving fingerprints representing video sub-sequences and introducing a simple metric for the matching of video sub-sequences. Experimental results show that the proposed algorithm overall outperforms three related duplication forgery detection algorithms in terms of computational efficiency, detection accuracy and robustness against common video operations like compression and brightness change.

Copy-Move Forgery Detection Using DyWT

2017 ◽  
Vol 8 (2) ◽  
pp. 1-9
Author(s):  
Choudhary Shyam Prakash ◽  
Sushila Maheshkar
Keyword(s):  
Wavelet Transform ◽  
Input Image ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
High Similarity ◽  
Dyadic Wavelet ◽  
Region Duplication ◽  
Copy Move Forgery Detection ◽  
Duplication Detection ◽  
Better Than

In this paper, we proposed a passive method for copy-move region duplication detection using dyadic wavelet transform (DyWT). DyWT is better than discrete wavelet transform (DWT) for data analysis as it is shift invariant. Initially we decompose the input image into approximation (LL1) and detail (HH1) sub-bands. Then LL1 and HH1 sub-bands are divided into overlapping sub blocks and find the similarity between the blocks. In LL1 sub-band the copied and moved blocks have high similarity rate than the HH1 sub-band, this is just because, there is noise inconsistency in the moved blocks. Then we sort the LL1 sub-band blocks pair based on high similarity and in HH1 blocks are sorted based on high dissimilarity. Then we apply threshold to get the copied moved blocks. Here we also applied some post processing operations to check the robustness of our method and we get the satisfactory results to validate the copy move forgery detection.

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