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arXiv Computer Vision By Sarim Hashmi, Abdelrahman Elsayed, Mohammed Talha Alam, Samuele Poppi, Nils Lukas

Certification of Real Images through Calibrated Content Authentication

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The paper evaluates twenty deepfake detectors against ten recent generators, finding that accuracy has dropped from 99.5% to 76% and that adversarial perturbations can reduce detector performance to below 2%. It proposes a new detection paradigm that certifies authenticity only when a faithful reconstruction by a known generator is impossible, and shows that this calibrated detector can limit false certifications to at most 1% while remaining robust to bounded‑perturbation attacks. The study also highlights the erosion of post‑hoc verifiability, noting that newer generators can reproduce a larger fraction of previously unreplicable images.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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