# A Comprehensive Dataset for Human vs. AI Generated Image Detection

arXiv: https://arxiv.org/abs/2601.00553 · HTML: https://mlsanity.com/paper/2601.00553/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 2 Jan 2026.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 13 of 13)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [The Creation and Detection of Deepfakes: A Survey](https://mlsanity.com/paper/2004.11138/) (2020)
- [A Sanity Check for AI-generated Image Detection](https://mlsanity.com/paper/2406.19435/) (2024)
- [Intriguing properties of synthetic images: from generative adversarial networks to diffusion models](https://mlsanity.com/paper/2304.06408/) (2023)
- [Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images](https://mlsanity.com/paper/2304.13023/) (2023)
- [WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection](https://mlsanity.com/paper/2402.11843/) (2024)
- [TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter](https://mlsanity.com/paper/2306.08310/) (2023)
- [AI-generated Image Detection: Passive or Watermark?](https://mlsanity.com/paper/2411.13553/) (2024)
- [Detecting AI-Generated Images via CLIP](https://mlsanity.com/paper/2404.08788/) (2024)
- [Multimodal Benchmarking and Recommendation of Text-to-Image Generation Models](https://mlsanity.com/paper/2505.04650/) (2025)

## Built on by (most cited first, 4 of 4)

- [NAU-QMUL: Utilizing BERT and CLIP for Multi-modal AI-Generated Image Detection](https://mlsanity.com/paper/2602.23863/) (2026)
- [LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection](https://mlsanity.com/paper/2607.25962/) (2026)
- [When Eyes Betray AI: Social Gaze Consistency as a Semantic Cue for AI-Generated Image Detection](https://mlsanity.com/paper/2605.27348/) (2026)
- [Frequency-Domain AI-Generated Image Detection: Exploring Decoder and Channel Attention for Feature Refinement](https://mlsanity.com/paper/2609.31723/) (2026)

## Abstract (arXiv)

Multimodal generative AI systems like Stable Diffusion, DALL-E, and MidJourney have fundamentally changed how synthetic images are created. These tools drive innovation but also enable the spread of misleading content, false information, and manipulated media. As generated images become harder to distinguish from photographs, detecting them has become an urgent priority. To combat this challenge, we release MS COCOAI, a novel dataset for AI generated image detection consisting of 96000 real and synthetic datapoints, built using the MS COCO dataset. To generate synthetic images, we use five generators: Stable Diffusion 3, Stable Diffusion 2.1, SDXL, DALL-E 3, and MidJourney v6. Based on the dataset, we propose two tasks: (1) classifying images as real or generated, and (2) identifying which model produced a given synthetic image. The dataset is available at https://huggingface.co/datasets/Rajarshi-Roy-research/Defactify_Image_Dataset.