A Comprehensive Dataset for Human vs. AI Generated Image Detection
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910252897665024 |
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| author | Roy, Rajarshi Aziz, Ashhar Bajpai, Shashwat Imanpour, Nasrin Singh, Gurpreet Biswas, Shwetangshu Wanaskar, Kapil Patwa, Parth Ghosh, Subhankar Dixit, Shreyas Pal, Nilesh Ranjan Rawte, Vipula Garimella, Ritvik Das, Amitava Sheth, Amit Jena, Gaytri Sharma, Vasu Reganti, Aishwarya Naresh Jain, Vinija Chadha, Aman |
| author_facet | Roy, Rajarshi Aziz, Ashhar Bajpai, Shashwat Imanpour, Nasrin Singh, Gurpreet Biswas, Shwetangshu Wanaskar, Kapil Patwa, Parth Ghosh, Subhankar Dixit, Shreyas Pal, Nilesh Ranjan Rawte, Vipula Garimella, Ritvik Das, Amitava Sheth, Amit Jena, Gaytri Sharma, Vasu Reganti, Aishwarya Naresh Jain, Vinija Chadha, Aman |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_00553 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Dataset for Human vs. AI Generated Image Detection Roy, Rajarshi Aziz, Ashhar Bajpai, Shashwat Imanpour, Nasrin Singh, Gurpreet Biswas, Shwetangshu Wanaskar, Kapil Patwa, Parth Ghosh, Subhankar Dixit, Shreyas Pal, Nilesh Ranjan Rawte, Vipula Garimella, Ritvik Das, Amitava Sheth, Amit Jena, Gaytri Sharma, Vasu Reganti, Aishwarya Naresh Jain, Vinija Chadha, Aman Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | A Comprehensive Dataset for Human vs. AI Generated Image Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2601.00553 |