A Deep Learning-Based Approach for Image Forgery Detection and Classification Using YOLO and CNN

A Deep Learning-Based Approach for Image Forgery Detection and Classification Using YOLO and CNN

A.Durga Bhavani

Computer Science and Engineering

Sasi Institute of Technology and Engineeering Tadepalligudem, INDIA

S.Durga Shankar

Computer Science and Engineering

Sasi Institute of Technology and Engineeering Tadepalligudem, INDIA

Ch.Aditya

Computer Science and Engineering

Sasi Institute of Technology and Engineeering Tadepalligudem, INDIA

D.Vijaya Vani

Computer Science and Engineering

Sasi Institute of Technology and Engineeering Tadepalligudem, INDIA

J.Hima Venkata Madhuri

Computer Science and Engineering

Sasi Institute of Technology and Engineeering Tadepalligudem, INDIA

Abstract –

and  AI-assisted  image   manipulation  tools,  which

The emergence of advanced image manipulation techniques in the digital age has made it extremely difficult to detect and categorize forged images. In order to identify and classify image forgeries, this study proposes a deep learning-based method that combines a Convolutional Neural Network (CNN) classifier with the You Only Look Once (YOLO) object detection model. The suggested system uses sophisticated feature extraction techniques to categorize images into authentic, copy-move, spliced, and deepfake forgeries after being trained on a custom dataset of manipulated images. While CNN-based classification offers accurate categorization, our experimental results show how reliable and effective YOLO is at locating forgery regions. Because of its high accuracy, the suggested model is a useful tool for security and digital forensics applications.

Keywords: Image Forgery, YOLO, CNN, Deep Learning, Classification, Digital Forensics