Revealing Image Deepfakes: A Convolutional Neural Network Approach Leveraging VGG-16 Mode

Digital image forgery has become a prevalent issue in modern technology-driven sphere, where sophisticated editing tools allow malicious users to create misleading and deceptive visual content. To deal with this growing problem, deep learning techniques have emerged as a powerful tool for both preventing and detecting digital image manipulation. In the proposed project focussing on developing deep learningbased forgery detection methods that can accurately identify manipulated images and to localize the forged part in the image using different methodologies, including VGG-16 which is a standard deep Convolutional Neural Networks (CNN),U-net, to build robust detection models capable of detecting a wide range of forgery techniques. Evaluating the effectiveness of our proposed forgery detection model, then building a comprehensive dataset consisting of authentic and forged images with diverse manipulation techniques. Furthermore, the proposed project addresses the challenge of detecting forged images that are altered to bypass existing detection mechanisms.