Open AccessCopy-Move Forgery Detection in Digital Images using Neural Network
Author(s) -
Jigna Patel,
Devika Bhatt
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.d1716.029420
Subject(s) - computer science , artificial intelligence , image (mathematics) , computer vision , digital image , counterfeit , block (permutation group theory) , image editing , key (lock) , feature detection (computer vision) , software , image processing , pattern recognition (psychology) , computer security , mathematics , geometry , political science , law , programming language
Due to easy availability of image editing software applications, many of the digital images are tempered, either to hide some important facts of the image or just to enhance the image. Hence, the integrity of the image is compromised. Thus, in order to preserve the authenticity of an image, it is necessary to develop some algorithms to detect counterfeit parts of an image, if there is any. Two kinds of classic methods exist for the detection of forgery: the key- point based method in which major key points of the image is found and forged part is detected and the block based method that locates the forged part by sectioning the whole image into blocks. Unlike these two classic methods that require multiple stages, our proposed CNN solution provides better image forgery detection. Our experimental results revealed a better forgery detection performance than any other classic approaches.