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LAION-Mobile: Evaluating Deepfake Detectors On One Million Smartphone Photos — arXiv:2609.11134

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.11134
License (as archived): CC0-1.0
Archived: 2026-10-16T06:35:16.248Z
SHA-256 of the archived text: ecdb315f8dcfec42bb596c539e648950c4549914fcf36d846b24b02e0caeb477
Stable URL of this page: https://metraipolis.com/documento/dc_7b8a71d2dd83

How to cite

LAION-Mobile: Evaluating Deepfake Detectors On One Million Smartphone Photos — arXiv:2609.11134 — arXiv (en), https://arxiv.org/abs/2609.11134 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7b8a71d2dd83 (Metraipolis archive dc_7b8a71d2dd83, sha256 ecdb315f8dcfec42bb596c539e648950c4549914fcf36d846b24b02e0caeb477)

BibTeX

@misc{dc7b8a71d2dd83,
  title = {LAION-Mobile: Evaluating Deepfake Detectors On One Million Smartphone Photos — arXiv:2609.11134},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.11134},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7b8a71d2dd83; sha256 ecdb315f8dcfec42bb596c539e648950c4549914fcf36d846b24b02e0caeb477},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.11134. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Most Deepfake detectors report near-perfect AUC scores on their reference benchmarks. However, a recent ICML position paper argues that these evaluations collectively neglect the impact of modern smartphone photography: the widely used on-device neural image-signal processing pipelines (like multi-sensor fusion or noise and motion-blur suppression) increasingly shift the imaging paradigm from simple lens projections towards computational photography. Hence, devices actually generate, rather than record photos. This increases the risk that deepfake detectors may flag ordinary phone photos as fake. Due to the lack of large-scale datasets containing images from modern smartphones, this hypothesis has so far only been tested in small proof-of-concept studies. The aim of this paper is to close this gap. We introduce LAION-Mobile, an open dataset containing about 1 million smartphone images with EXIF metadata distilled from re-LAION-5B. Evaluating twelve state-of-the-art deepfake detectors with their original paper checkpoints on a 9,115-image evaluation sample of this pool (DIRE on 738), we report three key findings: (i) On modern AI content no detector exceeds AUC 0.624, and five of twelve fall below chance. (ii) Real-photo false-alarm rates are an artefact of threshold calibration: thresholds fitted on legacy GAN data make several detectors look deployable (less than 11 percent FPR), yet the same detectors flag 17-91 percent of real photos once the identical criterion is refit on modern content. (iii) Consequently, no detector both beats chance on modern AI content and keeps a deployable real-photo false-alarm rate. Mirroring the device mix of web collections, the corpus probes the first neural-ISP generation (2018-2020); current flagships are essentially absent, leaving the modern-ISP regime as the open gap.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-11 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.11134 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T06:06:28.755Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).