Findings of the Counter Turing Test: 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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| author | Roy, Rajarshi Imanpour, Nasrin Aziz, Ashhar Bajpai, Shashwat Singh, Gurpreet Biswas, Shwetangshu Wanaskar, Kapil Patwa, Parth Ghosh, Subhankar Dixit, Shreyas Pal, Nilesh Ranjan Rawte, Vipula Garimella, Ritvik Das, Amitava Sheth, Amit Sharma, Vasu Reganti, Aishwarya Naresh Jain, Vinija Chadha, Aman |
| author_facet | Roy, Rajarshi Imanpour, Nasrin Aziz, Ashhar Bajpai, Shashwat Singh, Gurpreet Biswas, Shwetangshu Wanaskar, Kapil Patwa, Parth Ghosh, Subhankar Dixit, Shreyas Pal, Nilesh Ranjan Rawte, Vipula Garimella, Ritvik Das, Amitava Sheth, Amit Sharma, Vasu Reganti, Aishwarya Naresh Jain, Vinija Chadha, Aman |
| contents | The rapid advancements in generative AI technologies, such as Stable Diffusion, DALL-E, and Midjourney, have significantly transformed the creation of synthetic visual content. While these models enable innovation across industries, they also pose serious challenges, including misinformation, disinformation, and biased content generation. The increasing realism of AI-generated images makes their detection a pressing concern for researchers, policymakers, and industry stakeholders.
In this paper, we present the findings of the Defactify 4.0 workshop, which introduced the Counter Turing Test (CT2) for AI-Generated Image Detection. The competition consisted of two key tasks: (1) binary classification of images as either AI-generated or real and (2) identification of the specific generative model responsible for an AI-generated image. To support both tasks, we employed the MS COCOAI dataset, a benchmark of 96000 real and synthetic images generated by five state-of-the-art models alongside real images from MS COCO.
Participants employed diverse detection strategies, including convolutional neural networks (CNNs), Vision Transformers (ViTs), frequency-based analysis, contrastive learning, and multimodal techniques. The results demonstrated that while AI-generated images can be detected with high accuracy (F1-score > 0.83), identifying the exact model used remains significantly more challenging (highest F1-score: 0.4986). These findings highlight the need for improved model fingerprinting, adversarial robustness, and real-time detection mechanisms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20787 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Findings of the Counter Turing Test: AI-Generated Image Detection Roy, Rajarshi Imanpour, Nasrin Aziz, Ashhar Bajpai, Shashwat Singh, Gurpreet Biswas, Shwetangshu Wanaskar, Kapil Patwa, Parth Ghosh, Subhankar Dixit, Shreyas Pal, Nilesh Ranjan Rawte, Vipula Garimella, Ritvik Das, Amitava Sheth, Amit Sharma, Vasu Reganti, Aishwarya Naresh Jain, Vinija Chadha, Aman Computer Vision and Pattern Recognition The rapid advancements in generative AI technologies, such as Stable Diffusion, DALL-E, and Midjourney, have significantly transformed the creation of synthetic visual content. While these models enable innovation across industries, they also pose serious challenges, including misinformation, disinformation, and biased content generation. The increasing realism of AI-generated images makes their detection a pressing concern for researchers, policymakers, and industry stakeholders. In this paper, we present the findings of the Defactify 4.0 workshop, which introduced the Counter Turing Test (CT2) for AI-Generated Image Detection. The competition consisted of two key tasks: (1) binary classification of images as either AI-generated or real and (2) identification of the specific generative model responsible for an AI-generated image. To support both tasks, we employed the MS COCOAI dataset, a benchmark of 96000 real and synthetic images generated by five state-of-the-art models alongside real images from MS COCO. Participants employed diverse detection strategies, including convolutional neural networks (CNNs), Vision Transformers (ViTs), frequency-based analysis, contrastive learning, and multimodal techniques. The results demonstrated that while AI-generated images can be detected with high accuracy (F1-score > 0.83), identifying the exact model used remains significantly more challenging (highest F1-score: 0.4986). These findings highlight the need for improved model fingerprinting, adversarial robustness, and real-time detection mechanisms. |
| title | Findings of the Counter Turing Test: AI-Generated Image Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.20787 |