Fighting Fake Visual Media: A Study of Current and Emerging Methods for Detecting Image and Video Tampering

Abstract

The increasing number of digital media users has led to a rising number of instances of visual media being manipulated for various purposes, including spreading false news and misinformation. This has made the authenticity of visual images and videos very challenging to maintain. It is therefore important that there are effective methods to detect image and video tampering in order to ensure that their authenticity is maintained. This paper aims to provide a comprehensive analysis of the current state of the art in this field. The paper presents an overview of the various techniques that are used to identify image and video tampering, such as machine learning-based methods, digital signatures, and statistical techniques. It also explores the limitations and strengths of these approaches, as well as the challenges they face when it comes to detecting fraud on social media platforms. It also covers the various emerging techniques that are being used to detect video and image tampering, such as deep learning based and blockchain based approaches. It provides an overview of their capabilities and limitations and also provides guidelines for identifying and preventing visual media tampering, as well as how to ensure that the authenticity of these images and videos is maintained. It also discusses how to educate the public about the harmful effects of fake visual media.