A Comprehensive Dataset for Human vs. AI Generated Image Detection

Rajarshi Roy, Nasrin Imanpour, Ashhar Aziz, Shashwat Bajpai, Gurpreet Singh +14 more
1/2/2026
cs.CVcs.AI

No official code found. Best implementation: kothariprakhar/a-comprehensive-dataset-for-human-vs-ai-generated--impl (0 stars, last commit 2026-01-07).

Abstract

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.

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Implementation of "A Comprehensive Dataset for Human vs. AI Generated Image Detection"

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@article{roy2026comprehensive,
  title  = {A Comprehensive Dataset for Human vs. AI Generated Image Detection},
  author = {Rajarshi Roy and Nasrin Imanpour and Ashhar Aziz and Shashwat Bajpai and Gurpreet Singh and Shwetangshu Biswas and Kapil Wanaskar and Parth Patwa and Subhankar Ghosh and Shreyas Dixit and Nilesh Ranjan Pal and Vipula Rawte and Ritvik Garimella and Gaytri Jena and Vasu Sharma and Vinija Jain and Aman Chadha and Aishwarya Naresh Reganti and Amitava Das},
  year   = {2026},
  eprint = {2601.00553},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.00553v1}
}

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