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Design of a Generative AI Image Similarity Test Application and Handmade Images Using Deep Learning Methods

Rancang Bangun Aplikasi Uji Kemiripan Gambar AI Generative dan Gambar Buatan Tangan Menggunakan Metode Deep Learning




Deep Learning, Ai Generative, Transformers, BEiT, Image Classification


This research discusses the development of an application to test the similarity between AI Generative images and handmade images using deep learning methods. AI technology has been applied to generative art through deep learning algorithms; however, there are still challenges related to the copyright and originality of AI Generative art. This research aims to develop an efficient model for classifying AI Generative art and handmade art. The classification model uses a Transformer approach, in particular the BEiT architecture. This architecture has shown excellent results in image classification tests, achieving a high F1 score in each test, indicating a good balance between precision and recall. It achieves 80% accuracy compared to previous methods using CNN and the VGG16 model. In contrast, the KNN method achieves approximately 64% accuracy in this study. Overall, the Transformer model shows superior performance compared to both the Convolutional Neural Network (CNN) and K-Nearest Neighbour (KNN) methods.


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