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Certification of Real Images through Calibrated Content Authentication

The paper evaluates deepfake detectors against recent generators and finds their accuracy declining over time, with adversarial attacks reducing performance significantly. It proposes a calibrated detection method that assesses plausibly deniable authenticity through faithful reconstruction by known generators, showing improved calibration and security thresholds against certain attacks.

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PublishedOctober 5, 2026Sarim Hashmi, Abdelrahman Elsayed, Mohammed Talha Alam
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WHY IT MAY MATTER

This method provides a calibrated approach to image authenticity verification, addressing the limitations of traditional detectors and offering a way to assess plausibly deniable authenticity through reconstruction.

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