Browsing by Author "Warnakulasooriya, A. I."

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    Deep Learning-Based Approach for Distinguishing Between AI-Generated and Human-Drawn Paintings
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Warnakulasooriya, A. I.; Rupasingha, R. A. H. M.; Kumara, B. T. G. S.
    With the increasing number of robust Artificial Intelligence (AI) art generation applications, more realistic AI-generated paintings (AIGPs) are emerging, creating a significant impact on artists. Due to the widespread acceptance of AIGPs, the cultural, historical, and monetary value of real masterpieces is becoming uncertain, raising concerns about the significance of human painters and their artistic techniques. To protect artists’ rights, it is crucial to differentiate AIGPs from human-drawn paintings (HDPs). Accordingly, the main objective of this research is to develop a Convolutional Neural Network (CNN) model that can automatically distinguish between AI-generated and human-drawn paintings without human intervention. Unlike previous studies that focused mainly on pixel-level analysis, the proposed model considers additional features such as edge patterns, object arrangements, pattern distributions, and gradient characteristics in painting classification. A diverse dataset of 3,000 paintings from the AI-ArtBench Dataset—comprising 1,500 AIGPs and 1,500 HDPs across 10 different art themes—was collected and preprocessed for this study. The AIGPs were generated in equal proportions using Latent Diffusion and Standard Diffusion Models. The implemented CNN model achieved an optimum classification accuracy of 90% with a training data size of 10%, while the ANN model exhibited 77% accuracy under the same conditions. Furthermore, models were compared using performance metrics such as precision, recall, F1-score, RMSE, and MAE. Through Gradient-weighted Class Activation Mapping (Grad-CAM), the key visual features that the CNN model used to distinguish AIGPs from HDPs were identified. These findings highlight the potential of automated systems in detecting AI-generated versus human-created artworks for authentication purposes. Future work will focus on analyzing model performance across different art styles and identifying the unique discriminative features associated with each.