Detecting Manipulated Online Rhinoplasty Images Using an Artificial Intelligence Facial Authenticity Localization (FAL) Model
Synopsis
This study provides the first large-scale application of an AI-based Facial Authenticity Localization (FAL) model to aesthetic-surgery media, analyzing 600 consecutive postoperative rhinoplasty photographs from RealSelf.com's public gallery and finding suspected digital manipulation in 19.5% (117/600; 95% CI, 16.3-22.7%), with a 1.5% false-positive rate (3/200; 95% CI, 0.51-4.32%) on 200 presumed-unedited clinical photographs and 100% sensitivity (50/50; 95% CI, 93.0-100%) on 50 Facetune-generated geometric warps.
Interpretation
It estimates the prevalence of digital manipulation in real-world rhinoplasty photographs: 19.5% (117/600; 95% CI, 16.3-22.7%) of images in RealSelf.com's public gallery showed nasal-region heatmap activations judged as suspected manipulation. Prior work lacked systematic quantification of manipulation in online aesthetic-surgery imagery; this study offers a first large-scale prevalence estimate using 600 consecutive, unfiltered photographs. Based on 600 consecutive photographs with no additional filtering and reported 95% confidence intervals, making it a descriptive prevalence estimate.
The FAL model showed a low false-positive rate on validation: 1.5% (3/200; 95% CI, 0.51-4.32%) among 200 presumed-unedited clinical photographs exported from RAW with minimal processing. It provides quantitative evidence of specificity in a clinical photography setting rather than only a conceptual demonstration. Validation used 200 presumed-unedited clinical photographs, a limited sample with a wide confidence interval.
FAL was highly sensitive to Facetune-generated geometric warps: all 50 intentionally warped clinical photographs were detected, for 100% sensitivity (50/50; 95% CI, 93.0-100%). It shows the method can capture a specific manipulation type, Facetune-style geometric warping, rather than only flagging generic anomalies. Sensitivity was assessed on 50 intentionally warped clinical photographs, a small sample limited to Facetune-generated geometric warps.
The study proposes integrating automated authenticity checks into clinical photography and platform workflows to improve transparency. It extends the detection results from technical metrics to an application vision for clinical photography standards and platform content governance. This is an inferential recommendation based on the prevalence and validation results; no actual deployment or intervention outcome is reported.
Perspective
The results apply to 600 consecutive postoperative rhinoplasty photographs captured from RealSelf.com's public gallery in October 2025, and to validation settings composed of RAW-exported, minimally processed clinical photographs and Facetune-generated geometric warps; they are relevant to patients, clinicians, and platform operators interested in the authenticity of online aesthetic-surgery imagery, and provide initial grounds for incorporating automated authenticity checks into clinical photography and platform workflows.
A careful reader would still watch how FAL performs on manipulation types other than Facetune geometric warps; whether prevalence and false-positive rates remain stable on larger, more diverse clinical and platform image sets; how agreement between heatmap activations and actual manipulation is defined; and what practical effects integrating automated checks into clinical photography and platform workflows would have on patient expectations and platform content governance.