Arts and Social Sciences Journal

ISSN: 2151-6200

Open Access

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


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.


Share this article

Google Scholar citation report
Citations: 1413

Arts and Social Sciences Journal received 1413 citations as per Google Scholar report

Indexed In

arrow_upward arrow_upward