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arxiv: 2601.00553 · v2 · pith:7QJVLBZDnew · submitted 2026-01-02 · 💻 cs.CV · cs.AI

A Comprehensive Dataset for Human vs. AI Generated Image Detection

classification 💻 cs.CV cs.AI
keywords datasetgeneratedimageimagessyntheticdiffusionstablebecome
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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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Cited by 2 Pith papers

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  1. Findings of the Counter Turing Test: AI-Generated Image Detection

    cs.CV 2026-05 unverdicted novelty 4.0

    The Counter Turing Test competition finds F1-scores above 0.83 for binary real-vs-AI classification but only 0.4986 at best for identifying the specific generative model.

  2. Findings of the Counter Turing Test: AI-Generated Image Detection

    cs.CV 2026-05 unverdicted novelty 4.0

    A competition using a new 50k-image dataset found high accuracy in binary real-vs-AI detection but only modest success in identifying the exact generative model.