GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment

arXiv cs.CV / 4/29/2026

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Key Points

OpenAI’s GPT-Image-2 release is presented as a major inflection point, making it increasingly difficult to distinguish synthetic AI imagery from photographic reality.
Researchers introduce and publish the GPT-Image-2 Twitter Dataset, containing 10,217 confirmed GPT-image-2 images collected from public Twitter/X posts during the first week after the April 21, 2026 deployment.
The dataset creation combines Twitter API v2 collection with multilingual text heuristics, automated “Made with AI” badge verification, and model-name variant matching, using a six-day curation window.
The paper analyzes the images using CLIP-based taxonomy, OCR (82.0% show detectable text), face detection (59.2% with 22,583 total faces), and semantic clustering (137 clusters).
A major finding is that C2PA content credentials are stripped by Twitter’s CDN on upload, preventing cryptographic provenance verification for images sourced from social media.

Abstract

The release of GPT-image-2 by OpenAI marks a watershed moment in AI-generated imagery: the boundary between photographic reality and synthetic content has never been more difficult to discern. We introduce the GPT-Image-2 Twitter Dataset, the first published dataset of GPT-image-2 generated images, sourced from publicly available Twitter/X posts in the immediate aftermath of the model's April 21, 2026 release. Leveraging the Twitter API v2 and a multi-stage curation pipeline spanning multilingual text heuristics (English, Japanese, and Chinese), browser-automated Twitter "Made with AI" badge verification, and model name variant matching, we curate 10,217 confirmed GPT-image-2 images from 27,662 collected records over a six-day window. We characterize the dataset across four analyses: CLIP-based zero-shot subject taxonomy, OCR text legibility (82.0% of images contain detectable text), face detection (59.2% of images, 22,583 total faces), and semantic clustering (137 CLIP ViT-L/14 clusters). A key negative result is that C2PA content credentials are systematically stripped by Twitter's CDN on upload, rendering cryptographic provenance verification infeasible for social-media-sourced AI images. The dataset and all curation code are released publicly.