Organic or diffused: Can we distinguish human art from ai-generated images?
Authors: AYJ Ha, J Passananti, R Bhaskar, S Shan
Published: 2024
Publication: Proceedings of the ..., 2024 - dl.acm.org
The paper investigates the effectiveness of different approaches, including both human and automated detectors, in distinguishing human art from AI-generated images, finding that a combination of methods offers the best performance despite persistent weaknesses.
Methods: Comparison of human art across 7 styles with AI-generated images from 5 generative models, assessed using 5 automated detectors and 3 human groups (crowdworkers, professional artists, expert artists).
Key Findings: Detection accuracy and robustness of human and automated methods in identifying AI-generated images under benign and adversarial conditions.
Limitations: Weaknesses persist in both human and automated detectors; Hive struggles against adversarial perturbations, whereas expert artists show higher false positive rates.
Institution: University of California Santa Barbara, The University of Chicago, Institute of Education, University College London
Research Area: Human-Computer Interaction (HCI), Generative AI, Digital Forensics
Discipline: Human-Computer Interaction (HCI), Generative AI, Digital Forensics
Sample Size: 3993 participants
Citations: 52
DOI: 10.1145/3658644.3670306