ENHANCING DIGITAL IMAGE FORGERY DETECTION USING TRANSFER LEARNING

In today’s digital world, images are often shared and used as evidence in news, legal cases, and social media. However, it has become easier to manipulate images using editing tools, which can lead to false information and serious consequences. Detecting these changes, known as image forgery, is important to make sure images are trustworthy. Traditional methods for detecting image tampering often struggle with accuracy, especially when the edits are small or done carefully. These methods also require a lot of manual work and may not keep up with the fast-growing technology of image editing. This project presents a smart system that uses transfer learning to detect forged images. Transfer learning is a machine learning method that uses pre-trained deep learning models, like CNNs (Convolutional Neural Networks), to understand and analyze images more effectively. These models can find tiny changes in images that are hard to notice with the human eye. The system works by checking for common types of forgery, such as copy-move, splicing, and removal. It is trained on real and tampered images, helping it learn the patterns of forgery. Once trained, it can quickly check new images and say whether they are original or fake.By automating the detection process, this project saves time, improves accuracy, and helps fight the spread of false visuals. It provides a reliable tool for media, law enforcement, and digital forensics to make sure images are real and trustworthy.