Image Forgery Detection Based on Fusion of light weight Deep Learning Models

Abstract

Image manipulation has increased in popularity as a result of the software that is readily available for altering photos. Since the altered photographs cannot be distinguished with the human eye, they are spreading on numerous platforms, causing confusion and spreading rumours.Researchers have been working on several methods for the more accurate detection of altered photographs as a result.Better accuracy is provided by neural networks' ability to extract intricate hidden properties from images. In contrast to conventional methods of counterfeit detection, a deep learning model automatically creates the necessary features; as a result, it has emerged as the newest field of study in image forgery.In this research, we suggest an approach for detecting image forgery that is fusion-based. SqueezeNet, MobileNetV2, and ShuffleNet—three compact deep learning models—are the foundation of the decision fusion.Two phases comprise the implementation of the fusion decision system. The evaluation of the forgeries of the photos begins with the pretrained weights of the lightweight deep learning models. The outcomes of the counterfeiting of the photos are compared with the pre-trained models using the ne-tuned weights, second.In comparison to state-of-the-art techniques, the experimental results show that the fusion-based decision strategy delivers higher accuracy.The paper initially discusses various types of image forgery techniques and later on compares different approaches involving neural networks to identify forged images