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.
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