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- W4367462107 abstract "Digital images can be tempered or modified very easily with many tools available on the internet. Nowadays some software not only enhance and retouch but also copy-move objects of the image in such a way that is hard to differentiate. Image forgery detection deals with the recognition of tempering like splicing, copy-move, and other modifications done on the image. One of the most common ways of passive forgery detection is the block-based copy-move technique. In this paper, forgery detection is achieved in two parts. In the first part, Copy-Move forgery is classified by support vector machine (SVM) and features are extracted by local binary pattern (LBP) and discrete cosine transform (DCT). However, the region of forgery is calculated in the second part with the help of the Gray-level Co-occurrence Matrix (GLCM) through overlapping blocks. The whole experiment and evaluations are performed on the CoMoFoD image data set. The performance is measured in terms of accuracy and sensitivity. The promising result indicates the effectiveness of the model with enhanced accuracy of 97.82%." @default.
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- W4367462107 date "2023-01-01" @default.
- W4367462107 modified "2023-09-26" @default.
- W4367462107 title "Two-Stage Model for Copy-Move Forgery Detection" @default.
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- W4367462107 doi "https://doi.org/10.1007/978-981-19-8493-8_62" @default.
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