Article: Artificial intelligence-driven visual feature extraction and transfer learning for automatic identification of paintings and photographs Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.29 pp.1 - 18 Abstract: The fusion of art using and artificial intelligence (AI) technology has revolutionised the creative landscape, introducing innovative techniques to produce and interpret visual art. AI has emerged as a powerful tool for generating hyper-realistic images and mimicking traditional art styles, raising profound questions about the authenticity and originality of artistic creations. As AI-generated photographs grow increasingly indistinguishable from human-made paintings. The research examines how advanced deep learning techniques enable accurate human vs. AI artwork differentiation through experimental model evaluations. Our research combined the previously trained VGG19 model with a specially developed CNN to discriminate between different image categories. The VGG19 model validated image feature extraction capabilities but the proposed CNN upgraded this performance with domain-based visual art recognition properties. Extensive testing of a curated AI-generated photograph and human-made painting dataset enabled the proposed CNN model to reach a 95% classification success rate, which outperformed the baseline VGG19 model results. Inderscience Publishers - linking academia, business and industry through research

Title: Artificial intelligence-driven visual feature extraction and transfer learning for automatic identification of paintings and photographs

Authors: Jia Wu; Hongyi Li

Addresses: School of Art and Design, Geely University of China, Chengdu, 641423, China ' College of Art and Design, Geely College, Chengdu, 641423, China

Abstract: The fusion of art using and artificial intelligence (AI) technology has revolutionised the creative landscape, introducing innovative techniques to produce and interpret visual art. AI has emerged as a powerful tool for generating hyper-realistic images and mimicking traditional art styles, raising profound questions about the authenticity and originality of artistic creations. As AI-generated photographs grow increasingly indistinguishable from human-made paintings. The research examines how advanced deep learning techniques enable accurate human vs. AI artwork differentiation through experimental model evaluations. Our research combined the previously trained VGG19 model with a specially developed CNN to discriminate between different image categories. The VGG19 model validated image feature extraction capabilities but the proposed CNN upgraded this performance with domain-based visual art recognition properties. Extensive testing of a curated AI-generated photograph and human-made painting dataset enabled the proposed CNN model to reach a 95% classification success rate, which outperformed the baseline VGG19 model results.

Keywords: artificial intelligence; deep learning; art classification; computer vision; convolutional neural network; CNN; AI-generated images; artificial intelligence; photograph; painting.

DOI: 10.1504/IJICT.2025.147879

International Journal of Information and Communication Technology, 2025 Vol.26 No.29, pp.1 - 18

Received: 08 May 2025
Accepted: 28 May 2025
Published online: 05 Aug 2025 *

Title: Artificial intelligence-driven visual feature extraction and transfer learning for automatic identification of paintings and photographs

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