Identifying fake images through Hybrid CNN using FIDAC System

Abstract

Fake image detection has become an important problem in computer vision and machine learning because manipulated images are increasingly used for spreading false information. In this FIDAC system, a method is proposed, for detecting fake images using a combination of deep learning (specifically Convolutional Neural Networks, or CNNs) and traditional image processing techniques. This method extracts useful features from the image, such as color, texture, and statistical properties, and uses these features to decide if the image is real or fake. This approach is tested on a large set of real and fake images and it observed that it performs very well in terms of accuracy, precision, and recall. These results suggests that machine learning methods can be very effective in detecting fake images, with potential applications in social media content moderation, news verification, and forensic analysis.