An Efficient CNN Model to Detect Copy-Move Image Forgery

Recently, digital images have become used in many applications, where they the focus of image processing researchers. Image forgery represents one hot topic on which researchers prioritize their studies. We concentrate copy-move as a deceptive type. In forgery, part an is copied and placed same to produce image. This paper proposes accurate convolutional neural network(CNN) architecture for effective detection forgery. The proposed computationally lightweight with suitable number max-pooling layers. also present fast testing process 0.83 seconds every test. Many empirical experiments been conducted ensure efficiency model terms accuracy time. These were done benchmark datasets achieved 100% accuracy.