Detecting Manipulated Online Rhinoplasty Images Using an Artificial Intelligence Facial Authenticity Localization (FAL) Model

BACKGROUND: Publicly available before-and-after photos influence patient expectations and perceptions of surgical success. Online platforms host thousands of images, yet the authenticity of these representations remain unverified in an age where digitally altered photographs are increasingly common in aesthetic surgery. The Facial Authenticity Localization (FAL) model can detect subtle pixel-level irregularities indicative of digital tampering. This study represents the first large-scale application of an AI-based authenticity detector to aesthetic-surgery media. METHODS: The first 600 consecutive postoperative rhinoplasty photographs in RealSelf.com's public gallery (October 2025) were analyzed without additional filtering or selection. FAL was implemented locally on macOS via an Anaconda-Python environment with pretrained global and local weights. Heatmap outputs visualized probable regions of digital manipulation. Positives were defined as nasalregion heatmap activations. Specificity was estimated on 200 presumed-unedited clinical photographs exported from RAW with minimal processing; sensitivity was assessed on 50 clinical, intentionally warped photographs. RESULTS: Manipulation prevalence on RealSelf.com was 19.5% (117/600; 95% CI, 16.3-22.7%). The validation falsepositive rate was 1.5% (3/200; 95% CI, 0.51-4.32%). Sensitivity for detecting Facetune-generated geometric warps was 100% (50/50; 95% CI, 93.0-100%). CONCLUSIONS: AIbased geometric manipulation detection identifies suspicious edits in roughly one in five public rhinoplasty photos on a major platform. Validation suggests high specificity and sensitivity for Facetune-generated edits. Integration of automated authenticity checks into clinical photography and platform workflows may improve transparency.