AI vs. Authentic: Decoding Architectural Imagery

As AI becomes increasingly integrated into design processes, accurately distinguishing AI-generated architectural images from real photographs is crucial for effective communication and decision-making in the field. Aim: This study explored how experienced designers perceive and identify AI-generated images, focusing on the challenges they encounter and the visual cues they rely on to assess authenticity. Method: Employing a mixed methods approach, five designers (1–20 years of experience) from a single firm participated in an hour-long focus group session on the Miro platform. They examined 16 images—8 AI-generated and 8 real—and were asked to identify AI-generated visuals. Annotations and discussions were thematically analyzed to capture participants’ decision-making processes and patterns of observation. Result: Overall, participants correctly classified 65% of exterior images and 70% of interior images. Analysis revealed five recurrent themes: subtle distortions in spatial elements, distorted or “demon-like” human features, warped backgrounds and inconsistent perspectives, over-perfection that lacked real-world imperfections, and reliance on professional domain knowledge. Night shots and images containing people presented consistent difficulties, while architectural expertise bolstered participants’ confidence in detecting anomalies. Limitation: Time constraints, limited zoom functionality on the Miro platform, and occasional confusion with voting mechanics potentially reduced thoroughness and accuracy. Environmental factors, including early-finishers discussing progress, introduced additional distractions that may have biased responses. Conclusion: These findings highlight how architectural expertise, image content, and technological constraints shape the process of identifying AI-generated images. As part of a broader ongoing study also including participants without an architectural background, this research underscores the importance of examining how diverse user groups approach AI-generated visual content.