Tamper image detection using error level analysis and convolutional neural networks.

The development taking place in our world today includes all aspects of life, especially multimedia, and made dealing with images and information daily and easy to use. The reliance on multimedia information has also increased, as it has become easier for each user to obtain images and information. Images can be manipulated through editing programs, and this can be done with ease and professionalism. The objective of this research is to verify digital images and detect forgery, Forgery images have many types of methods like splicing, copy move and retouching images. In this research, we suggest an effective way to detect image splicing forgery by using the error level analysis algorithm (ELA) and relying on deep learning using the convolution neural network. The proposed method was applied to two databases: (CASIAV0.1 & CASIAV 0.2) The proposed method outperformed similar studies in this area where it Applied with CASIA V0.1 database and achieved accuracy of 99% also applied with CASIA V0.2 database and achieved accuracy of up to 95%. [ABSTRACT FROM AUTHOR]