Detecting Copy Move Image Forgery using a Deep Learning Model: A Review

Abstract

The digital images can easily be manipulated using Software tool or mobile application these days. Dispersal of forgery images in social media is one of the prime threats and it has a prodigious impact. Most shared tampered images are based on duplicating some part of the image (copy move image forgery) and merging some portion of two different images (image splicing). Hence, trust in a digital image on social media is becoming extremely hard nowadays. The researchers are highly active in finding a solution for this challenge and there are several papers proposed with different approaches to solve this issue. Most of the suggestions revolve around deep learning models that are efficient and suitable to detect copy move images. This paper focusses on reviewing various Deep Convolution Neural Network (DCNN) approaches and hybrid Deep learning models in copy move image detection by comparative analysis of the experimental outcome of the different models presented for this issue. This research article compares various articles relating to our issue by means of a model, a dataset, and the characteristics of those articles.