# TextRich: A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2

arXiv: https://arxiv.org/abs/2606.19259 · HTML: https://mlsanity.com/paper/2606.19259/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in MLLM jailbreaks and adversarial robustness, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 26 of its 32 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM jailbreaks and adversarial robustness](https://mlsanity.com/mllm/mllm-jailbreaks-and-adversarial-robustness/)

## Builds on (most cited first, 26 of 26)

- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Towards Universal Fake Image Detectors that Generalize Across Generative Models](https://mlsanity.com/paper/2302.10174/) (2023)
- [InfographicVQA](https://mlsanity.com/paper/2104.12756/) (2021)
- [GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image](https://mlsanity.com/paper/2306.08571/) (2023)
- [ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction](https://mlsanity.com/paper/2103.10213/) (2021)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [TextDiffuser: Diffusion Models as Text Painters](https://mlsanity.com/paper/2305.10855/) (2023)
- [Raising the Bar of AI-generated Image Detection with CLIP](https://mlsanity.com/paper/2312.00195/) (2023)
- [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://mlsanity.com/paper/2110.00061/) (2021)
- [CanvasVAE: Learning to Generate Vector Graphic Documents](https://mlsanity.com/paper/2108.01249/) (2021)
- [Any-Resolution AI-Generated Image Detection by Spectral Learning](https://mlsanity.com/paper/2411.19417/) (2024)
- [Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection](https://mlsanity.com/paper/2312.10461/) (2023)
- [ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection](https://mlsanity.com/paper/2302.11970/) (2023)
- [WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection](https://mlsanity.com/paper/2402.11843/) (2024)
- [Revisiting Tampered Scene Text Detection in the Era of Generative AI](https://mlsanity.com/paper/2407.21422/) (2024)
- [Deep Learning based Visually Rich Document Content Understanding: A Survey](https://mlsanity.com/paper/2408.01287/) (2024)
- [ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation](https://mlsanity.com/paper/2505.18668/) (2025)
- [PosterSum: A Multimodal Benchmark for Scientific Poster Summarization](https://mlsanity.com/paper/2502.17540/) (2025)
- [Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review](https://mlsanity.com/paper/2502.16586/) (2025)
- [AIForge-Doc: A Benchmark for Detecting AI-Forged Tampering in Financial and Form Documents](https://mlsanity.com/paper/2602.20569/) (2026)
- [GPT4o-Receipt: A Dataset and Human Study for AI-Generated Document Forensics](https://mlsanity.com/paper/2603.11442/) (2026)
- [SciFigDetect: A Benchmark for AI-Generated Scientific Figure Detection](https://mlsanity.com/paper/2604.08211/) (2026)
- [Present and Future Generalization of Synthetic Image Detectors](https://mlsanity.com/paper/2409.14128/) (2024)
- [Practical Analyses of How Common Social Media Platforms and Photo Storage Services Handle Uploaded Images](https://mlsanity.com/paper/2302.12133/) (2023)
- [Using Facebook for Image Steganography](https://mlsanity.com/paper/1506.02071/) (2015)

## Built on by (most cited first, 2 of 2)

- [ChatGPT Images 2.5 in the Wild: A Launch-Period Dataset and Detector Evaluation](https://mlsanity.com/paper/2609.15100/) (2026)
- [Trustworthy synthetic visual media: Evidence across the media lifecycle](https://mlsanity.com/paper/2609.34232/) (2026)

## Abstract (arXiv)

Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information. As recent multimodal image generation models become increasingly capable of synthesizing realistic textual content and structured visual designs, detecting AI-generated text-rich images has become an important challenge for digital trust and content authenticity. Existing benchmarks, however, largely focus on object-centric images and provide limited coverage of scenarios where textual semantics and layout organization are central. In this paper, we introduce TextRich, a multi-domain benchmark for detecting text-rich images generated by OpenAI's GPT-Image-2. The benchmark contains 12,095 images across six representative categories: commercial posters, infographic charts, academic posters, receipts, tables, and UI screenshots. Using this benchmark, we evaluate five representative AI-generated image detectors under a zero-shot setting and further explore the capability of a multimodal vision-language model for this task. Our results reveal substantial performance variations across text-rich domains, where existing AI-generated image detectors exhibit distinct strengths and failure modes. Although the strongest detector achieves competitive overall performance, it remains ineffective on certain structured categories and highly sensitive to JPEG compression. Vision-language models provide a promising complementary approach, but still struggle with highly structured text-rich images. These findings highlight the need for text- and layout-aware detection methods for modern AI-generated images. Our dataset is released at https://huggingface.co/datasets/Shuyiww/TextRich.