Can Visual Evidence Be Trusted? Challenges Generative Images Pose to the Integrity of Evidence in Building Research

Abstract

Site photographs, material micrographs, and construction records are often used directly as evidence for claims in building research, and generative artificial intelligence can now synthesize such images in their entirety or edit authentic ones locally. How this material is judged once it enters the evidence stream has not been tested empirically. Using ChatGPT Images 2.0, this study assembled 18 images covering three sources—authentic, fully AI-generated, and AI-modified—across three types of evidence content, and 151 participants judged the source of each image and rated its evidentiary credibility and acceptability. Overall identification accuracy was 36.9% against a chance level of 33.3%, and 63.6% of participants scored at or below chance. Fully AI-generated images were judged authentic in 55.1% of cases and authentic images were correctly identified in 51.8%. Stated confidence did not distinguish correctly from incorrect judgments, and AI images mistaken for authentic ones were rated close to genuine images in credibility and acceptability. Accuracy rose with building-related experience, from 32.3% among participants reporting none to 53.1% among those reporting six to ten years, but no group reached a level at which the image alone would be a dependable check of its provenance.