Forgery Localization in Images Using Deep Learning

Photography has become incredibly popular as a result of camera systems being widely accessible. Photos are essential to our everyday lives because they are so full of information. Consequently, there is a frequent need to enhance photos to extract more meaningful data. However, the availability of various technologies for image enhancement has also led to the proliferation of photo manipulation, contributing to the dis-semination of misinformation. The emergence of image forgeries has become a pressing concern. While conventional frameworks have been established throughout time to detect picture forgeries, the localization of image forgeries has been greatly impacted by the recent development of convolutional neural networks (CNNs), Among the challenging types of image forgeries is the splicing of images, where a segment of one image is inserted into other images. Existing literature on image forgery localization techniques reveals certain limitations, emphasizing the necessity to devise effective methods for accurately pinpointing forgeries in manipulated images. In this context, we propose a robust deep learning-based approach that employs image patches to detect forgery in an image. To determine if a pixel is part of a tampered zone, a deep neural network is trained with an extracted patch around each pixel in the picture. The approach that is being provided shows effectiveness in both identifying the altered region's border pixels and separating them from the remainder of the image. Rigorous evaluations of the technique have been conducted, and the experimental results, particularly on the CASIA 2.0 database, are highly encouraging.