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- W2799571569 abstract "Video forgery detection aims to distinguish video forgeries from original videos. Video forgery detection in the form of features extraction from clustering the frames and matched with original videos. Scale Invariant Feature Transform (SIFT) are improved for detection of copy move attacks. Image keypoints are extracted and multi-dimensional feature vector named as SIFT descriptor is generated for each keypoint. Then, these keypoints are matched using distance among their descriptors. The experimental results show that our proposed algorithm has good at detection of copy move attacks. Total percentage of forged identify which frame to be forged and designed application as window based application with image processing techniques." @default.
- W2799571569 created "2018-05-17" @default.
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- W2799571569 date "2018-04-20" @default.
- W2799571569 modified "2023-09-25" @default.
- W2799571569 title "VIDEO FOREGERY DEDUCTION OF INTERFRAME DUPLICATION" @default.
- W2799571569 doi "https://doi.org/10.26483/ijarcs.v9i2.5680" @default.
- W2799571569 hasPublicationYear "2018" @default.
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