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LAION-Mobile: Evaluating Deepfake Detectors On One Million Smartphone Photos

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The paper introduces LAION-Mobile, a 1‑million‑image dataset of modern smartphone photos with EXIF metadata, to test deepfake detectors in a realistic computational‑photography setting. Twelve state‑of‑the‑art detectors were evaluated on a 9,115‑image subset, revealing that none achieved an AUC above 0.624 on AI‑generated content and that many detectors’ false‑alarm rates spike when thresholds are recalibrated for modern smartphone imagery. The study shows that current detectors fail to maintain both high detection performance and low false‑alarm rates on contemporary smartphone photos, highlighting a critical gap in existing evaluation benchmarks.

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arXiv Computer Vision
Sep 11

LAION-Mobile: Evaluating Deepfake Detectors On One Million Smartphone Photos

The paper introduces LAION-Mobile, a new dataset of roughly one million smartphone images with EXIF metadata, designed to test deepfake detectors on modern computational photography content. Twelve state‑of‑the‑art detectors were evaluated on a 9,115‑image subset, revealing that none achieved an AUC above 0.624 on AI‑generated images and five performed worse than chance. The study also shows that threshold calibration on legacy data can hide high false‑alarm rates, with some detectors flagging 17–91 % of real photos when recalibrated for modern content.

By Achim von Stryk, Janis Keuper