JPEG-compression agnostic identification of generative art using explainable spatial domain features

In recent years, the art landscape has undergone a considerable transformation with the emergence of AI-powered generative art tools, challenging traditional notions of artistic authenticity and ownership. The exponential growth of generative artwork sharing on social media platforms has created an urgent need to protect artists' intellectual properties from impersonation, forgery, and style appropriation. This study introduces an innovative, lightweight detection framework that efficiently distinguishes AI-generated art from human-created artwork by analyzing spatial domain features using tree-based ensembles. The study focuses on two prominent generative image architectures, StyleGAN2-ADA and Stable Diffusion, to explore the method's effectiveness across various classes of probabilistic deep generative models while incorporating JPEG compression considerations to reflect real-world social media conditions. The framework was evaluated across a diverse dataset of 10,000 images, achieving a detection accuracy of 94.43 % for StyleGAN2-ADA and 97.97 % for Stable Diffusion outputs on average across varying quality factors (QF). A key limitation observed is the lack of cross-architecture generalization-classifiers trained on one generative model do not reliably detect outputs from others, highlighting the need for architecture-agnostic detection strategies for real-world deployment. These results demonstrate comparable or better performance to existing deep learning solutions, requiring significantly less computational resources and training data. The proposed approach represents a significant step towards digital art authentication, offering a practical solution for real-time detection of AI-generated artwork in social media environments. Future work will focus on expanding the framework's capabilities to address emerging generative models and developing and integrating tools for automatic art authentication across various social media platforms.