Detection of Tampered Images with Convolutional Neural Networks (VGG-16)

In this project, we centered around the robust detection of tampered images through the implementation of the VGG-16 algorithm, a Convolutional Neural Network (CNN) known for its effectiveness in image analysis. We curated a diverse dataset from Kaggle, incorporating additional custom images, and meticulously divided it into distinct training and testing sets. Leveraging the capabilities of the VGG-16 model, our system underwent rigorous training and evaluation, ultimately achieving an impressive result. This outcome establishes our project as a powerful forgery detection tool. the user interface, developed with Tkinter, offers a straightforward experience, enabling users to effortlessly upload images and receive prompt classification results clearly indicating whether an image is deemed “Authentic” or “Forged.” Through this project, we underscore the practical integration of deep learning methodologies into everyday scenarios, providing a user-friendly solution for forgery detection that is accessible to all.