How Many Real Photos Does It Take? Finite-Sample False-Alarm Control of Frozen AI-Image Detectors under Paired Camera-Processing Shift — Preregistration (v1.0)

Preregistration (v1.0, 2026-11-01) of a confirmatory study on finite-sample false-alarm control of six frozen AI-generated-image detectors when the processing chain of real photographs changes (denoising, learned restoration, JPEG, resizing, alternative demosaicing). Hypotheses: H1, a Neyman–Pearson threshold calibrated on a reference chain fails to keep the false-positive rate on other chains; H2, the number of real photographs needed to recalibrate (n ≥ 59 for α = δ = 0.05) and its cost in detection rate; H3, blind image-computed groups versus a single worst-group threshold. New data: 300 RAW photographs from MIT-Adobe FiveK and 306 images from 9 commercial generators (Community Forensics evaluation set). Deposited before any detector score is computed on the new data. Files: the preregistration and the frozen analysis script c2_analyse.py (SHA-256 f207ce83c2e616c3ccf62aa50c11f755d82ce2d6629f5be2bf869942efc24284), tested on simulated data only. No images and no model weights. No funding.