Detecting Copy-Move Forgery in Images Using Convolutional Neural Networks (CNNs)

Recently, digital images have become widely used in various fields, attracting the attention of researchers in the field of digital image processing. This research focuses on detecting a type of image forgery using Convolutional Neural Networks (CNN), specifically the copy-move forgery. In this forgery, a portion of the image is copied and pasted onto another part of the same image. The research proposes a CNN-based network structure for detecting copy-move forgery in images. The model is trained on different datasets previously used in this domain and then tested on a new dataset to reliably evaluate the efficiency of the proposed model. The model was trained on the (MICC-F2000) dataset and achieved an accuracy of up to 97.75%. It was also trained on the (CoMoFoD) dataset and achieved an accuracy of up to 92.85%. The results of testing the trained models on the new dataset indicate the superiority of the model trained on the (MICC-F2000) dataset. However, both models did not achieve high accuracy due to the fact that the forgery in the new dataset is unclear and difficult to detect with the naked eye.