..

கலை மற்றும் சமூக அறிவியல் இதழ்

ஐ.எஸ்.எஸ்.என்: 2151-6200

திறந்த அணுகல்
கையெழுத்துப் பிரதியை சமர்ப்பிக்கவும் arrow_forward arrow_forward ..

Using U-NET with Grasshopper Optimisation to Spot Image Forgery on Social Media

Abstract

Kalpuerty Derwe

In today's digital age, social media platforms have become a ubiquitous medium for sharing information, experiences, and images. However, this convenience has also given rise to image forgery, a form of digital manipulation where images are altered to deceive viewers. Detecting image forgery is crucial to maintaining trust and credibility on social media platforms. In this article, we explore the combination of U-Net, a deep learning architecture, and Grasshopper Optimization, a metaheuristic algorithm, to enhance the accuracy of image forgery detection. The proliferation of advanced image editing tools has made it increasingly difficult to differentiate between authentic and manipulated images. Image forgery can take many forms, such as splicing, copy-move, retouching, and more. These manipulated images can be used for malicious purposes, including spreading fake news, damaging reputations, and even inciting violence.

இந்தக் கட்டுரையைப் பகிரவும்

குறியிடப்பட்டது

arrow_upward arrow_upward