Biblio

This paper deals with the problem of image forgery detection because of the problems it causes. Where The Fake im-ages can lead to social problems, for example, misleading the public opinion on political or religious personages, de-faming celebrities and people, and Presenting them in a law court as evidence, may Doing mislead the court. This work proposes a deep learning approach based on Deep CNN (Convolutional Neural Network) Architecture, to detect fake images. The network is based on a modified structure of Xception net, CNN based on depthwise separable convolution layers. After extracting the feature maps, pooling layers are used with dense connection with Xception output, to in-crease feature maps. Inspired by the idea of a densenet network. On the other hand, the work uses the YCbCr color system for images, which gave better Accuracy of %99.93, more than RGB, HSV, and Lab or other color systems.