Automatic identification of AI-generated ceramic art images using convolutions-based neural networks models
Year: 2025
Type: article
Abstract: Ceramic art, deeply rooted in cultural heritage, has long been regarded as a symbol of craftsmanship and historical significance, often commanding substantial prices in the art market. However, with the rise of artificial intelligence (AI) and its ability to generate art that closely resembles human creations, distinguishing between authentic and AI-generated artworks has become a critical challenge. In this research work, deep learning base models including the proposed convolutional neural networks (CNNs) and pre-trained models are applied to identify ceramic arts, distinguishing between human prepared artefacts and AI-generated content (AIGC). There is no benchmark data set available to distinguish between real ceramic and AI-generated, therefore, the dataset has been prepared having two classes: authentic ceramic items (real) and AI-generated. The results obtained the highest accuracy of 98% by using CNN compared to pre-trained models, such as ResNet, VGG and AlexNet models. This study may help to identify the authenticity of digital artefacts in the digital era.
Authors: Yueming He, Xinyue Xu, Xiaobo Yu
Institution: Fuzhou University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410826653