Certification of Real Images through Calibrated Content Authentication

The paper examines the reliability of deepfake detectors, noting a decline in accuracy from 99.5% to 76% over four years and a drastic drop below 2% when adversarial perturbations are applied. It argues that content alone cannot determine provenance because generators can perfectly reproduce authentic images, so a new detection approach is proposed that certifies authenticity only if a generator cannot faithfully reconstruct the content. The authors demonstrate that their calibrated detector can limit false certifications to 1% while maintaining robustness against bounded‑perturbation attacks, though it struggles with arbitrary transformations.