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

• URL: http://arxiv.org/abs/2604.25370v1
• Date: Tue, 28 Apr 2026 08:35:51 GMT
• Title: GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment
• Abstract summary: GPT-image-2 by OpenAI marks a watershed moment in AI-generated imagery.<n>This dataset is the first published dataset of GPT-image-2 generated images sourced from publicly available Twitter/X posts.<n>We characterize the dataset across four analyses.
• Score: 5.547870160341159
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2 [5.27107161551086]
  We introduce a benchmark for detecting text-rich images generated by OpenAI's GPT Image 2.<n>The benchmark contains 8,602 images across six representative categories: commercial posters, infographics, academic posters, receipts, tables, and UI screenshots.<n>Using this benchmark, we evaluate five representative AI-generated image detectors in a zero-shot setting and analyze their overall, category-wise, and post-processing robustness.
  arXiv  Detail & Related papers  (2026-06-17T16:37:16Z)
• When the Forger Is the Judge: GPT-Image-2 Cannot Recognize Its Own Faked Documents [8.591475917096318]
  OpenAI's GPT-Image-2 has effectively erased the visual boundary between authentic and AI-edited document images.<n>We release AIForge-Doc v2, a paired dataset of 3,066 GPT-Image-2 document forgeries.
  arXiv  Detail & Related papers  (2026-04-28T04:33:26Z)
• AIForge-Doc: A Benchmark for Detecting AI-Forged Tampering in Financial and Form Documents [7.014776899553499]
  We present AIForge-Doc, the first dedicated benchmark targeting exclusively diffusion-model-based inpainting in financial and form documents with pixel-level annotation.<n>We benchmark three representative detectors -- TruFor, DocTamper, and a zero-shot GPT-4o judge -- and find that all existing methods degrade substantially.
  arXiv  Detail & Related papers  (2026-02-24T05:37:35Z)
• Could AI Trace and Explain the Origins of AI-Generated Images and Text? [53.11173194293537]
  AI-generated content is increasingly prevalent in the real world.<n> adversaries might exploit large multimodal models to create images that violate ethical or legal standards.<n>Paper reviewers may misuse large language models to generate reviews without genuine intellectual effort.
  arXiv  Detail & Related papers  (2025-04-05T20:51:54Z)
• CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI [58.35348718345307]
  Current efforts to distinguish between real and AI-generated images may lack generalization.<n>We propose a novel framework, Co-Spy, that first enhances existing semantic features.<n>We also create Co-Spy-Bench, a comprehensive dataset comprising 5 real image datasets and 22 state-of-the-art generative models.
  arXiv  Detail & Related papers  (2025-03-24T01:59:29Z)
• D-Judge: How Far Are We? Assessing the Discrepancies Between AI-synthesized and Natural Images through Multimodal Guidance [19.760989919485894]
  We construct a large-scale multimodal dataset, D-ANI, comprising 5,000 natural images and over 440,000 AIGI samples.<n>We then introduce an AI-Natural Image Discrepancy assessment benchmark (D-Judge) to address the critical question: how far are AI-generated images (AIGIs) from truly realistic images?
  arXiv  Detail & Related papers  (2024-12-23T15:08:08Z)
• Improving Multimodal Datasets with Image Captioning [65.74736570293622]
  We study how generated captions can increase the utility of web-scraped datapoints with nondescript text. Our experiments with using generated captions at DataComp's large scale (1.28B image-text pairs) offer insights into the limitations of synthetic text.
  arXiv  Detail & Related papers  (2023-07-19T17:47:12Z)
• LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding [85.39419609430453]
  This work enhances the current visual instruction tuning pipeline with text-rich images. We first use publicly available OCR tools to collect results on 422K text-rich images from the LAION dataset. We prompt text-only GPT-4 with recognized texts and image captions to generate 16K conversations, each containing question-answer pairs for text-rich images.
  arXiv  Detail & Related papers  (2023-06-29T17:08:16Z)
• TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter [29.77283532841167]
  We introduce TWIGMA, a dataset encompassing over 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter. We find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. We observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content.
  arXiv  Detail & Related papers  (2023-06-14T07:27:57Z)
• Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text [130.89493542553151]
  In-context vision and language models like Flamingo support arbitrarily interleaved sequences of images and text as input. To support this interface, pretraining occurs over web corpora that similarly contain interleaved images+text. We release Multimodal C4, an augmentation of the popular text-only C4 corpus with images interleaved.
  arXiv  Detail & Related papers  (2023-04-14T06:17:46Z)
• Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation [83.2079454464572]
  This paper describes our approach to the Image-Text Inconsistency Detection challenge of the DARPA Semantic Forensics (SemaFor) Program. We collect Twitter-COMMs, a large-scale multimodal dataset with 884k tweets relevant to the topics of Climate Change, COVID-19, and Military Vehicles. We train our approach, based on the state-of-the-art CLIP model, leveraging automatically generated random and hard negatives.
  arXiv  Detail & Related papers  (2021-12-16T03:37:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.