Open AccessMethods of Detecting Image forgery using convolutional neural network
Author(s) -
Alka Leekha,
Arpan Gupta,
Amit Kumar,
Tarun Chaudhary
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1831/1/012026
Subject(s) - computer science , convolutional neural network , image (mathematics) , artificial intelligence , digital image , set (abstract data type) , computer vision , hash function , pattern recognition (psychology) , image processing , computer security , programming language
Image Forgery means manipulation of the digital image so as to hide the necessary information or to convert the image into something which can be used for illegal mo-tives.Differentiating between an original image and a tampered image is a strenuous work for a human being. Machines can do that easily for large amount of data with the help of many artificial intelligence technique and mathematical approaches that are used to develop a system which will help in recognizing whether the image is tampered or not. In this paper Various models like CNN , Hashing , DCT, DWT will be discussed.This paper also discussed about the implementation of CNN done on a data set to figure out a model which is best suited for the development of an image forgery detection system.