LAION-Mobile: Evaluating Deepfake Detectors On One Million Smartphone Photos
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 hypot...
| Authors | Achim von Stryk, Janis Keuper |
|---|---|
| Primary category | cs.CV |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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