Abstract
In the modern day, protecting data against tampering is a significant task. One of the most common forms of information display has been digital photographs. Images may be exploited in a variety of contexts, including the military, security applications, intelligence areas, legal evidence, social media, and journalism. Digital picture forgeries involve altering the original images with strange patterns, which result in variability in the image's characteristics. Among the most challenging forms of image forgeries to identify is Copy Move Forgery (CMF). It occurs by copying a portion or piece of the picture and then inserting it again, but in a different place. When the actual content is unavailable, techniques for detecting fake content have been utilised in image security. This study presents a novel method for Copy Move Forgery Recognition (CMFR), which is mostly based on deep learning (DL) and hybrid optimization. The hybrid Grey Wolf Optimization and African Buffalo Optimization (GWO-ABO) using Convolution Neural Network (CNN) technique i.e., GWO-ABO-CNN is the foundation of the suggested model. The developed model extracts the features of images by convolution layers, and pooling layers; hereafter, the features are matched and detect CMF. The MICC-F220, SATs-130, and MICC-F600 datasets were three publicly accessible datasets to which this methodology has been implemented. To assess the model's efficacy, the outcomes of implementing the GWO-ABO-CNN model were contrasted with those of other approaches.