Visual Counter Turing Test (VCT^2): Discovering the Challenges for
  AI-Generated Image Detection and Introducing Visual AI Index (V_AI)
The proliferation of AI techniques for image generation, coupled with their
increasing accessibility, has raised significant concerns about the potential
misuse of these images to spread misinformation. Recent AI-generated image
detection (AGID) methods include CNNDetection, NPR, DM Image Detection, Fake
Image Detection, DIRE, LASTED, GAN Image Detection, AIDE, SSP, DRCT, RINE,
OCC-CLIP, De-Fake, and Deep Fake Detection. However, we argue that the current
state-of-the-art AGID techniques are inadequate for effectively detecting
contemporary AI-generated images and advocate for a comprehensive reevaluation
of these methods. We introduce the Visual Counter Turing Test (VCT^2), a
benchmark comprising ~130K images generated by contemporary text-to-image
models (Stable Diffusion 2.1, Stable Diffusion XL, Stable Diffusion 3, DALL-E
3, and Midjourney 6). VCT^2 includes two sets of prompts sourced from tweets by
the New York Times Twitter account and captions from the MS COCO dataset. We
also evaluate the performance of the aforementioned AGID techniques on the
VCT^2 benchmark, highlighting their ineffectiveness in detecting AI-generated
images. As image-generative AI models continue to evolve, the need for a
quantifiable framework to evaluate these models becomes increasingly critical.
To meet this need, we propose the Visual AI Index (V_AI), which assesses
generated images from various visual perspectives, including texture complexity
and object coherence, setting a new standard for evaluating image-generative AI
models. To foster research in this domain, we make our
https://huggingface.co/datasets/anonymous1233/COCO_AI and
https://huggingface.co/datasets/anonymous1233/twitter_AI datasets publicly
available.
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Nasrin Imanpour
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Shashwat Bajpai
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Shashwat Bajpai
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Shashwat1729
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Subhankar Ghosh
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Sainath Reddy Sankepally
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Abhilekh Borah
claimed_verified
2025-08-14T13:42:45.711Z
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Abhilekh Borah
user
abhilekhborah
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Hasnat Md Abdullah
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Nishoak Kosaraju
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Shreyas Dixit
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Ashhar Aziz
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Shwetangshu Biswas
claimed_verified
2025-03-02T20:19:28.742Z
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Shwetangshu Biswas
user
shwetangshub
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Vinija Jain
6746a7c80a7a7bc04551f5cb
Aman Chadha
claimed_verified
2024-11-27T22:20:00.163Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
6746a7c80a7a7bc04551f5cc
Amit Sheth
6746a7c80a7a7bc04551f5cd
Amitava Das
Visual Counter Turing Test (VCT^2): Discovering the Challenges for
  AI-Generated Image Detection and Introducing Visual AI Index (V_AI)
The proliferation of AI techniques for image generation, coupled with their
increasing accessibility, has raised significant concerns about the potential
misuse of these images to spread misinformation. Recent AI-generated image
detection (AGID) methods include CNNDetection, NPR, DM Image Detection, Fake
Image Detection, DIRE, LASTED, GAN Image Detection, AIDE, SSP, DRCT, RINE,
OCC-CLIP, De-Fake, and Deep Fake Detection. However, we argue that the current
state-of-the-art AGID techniques are inadequate for effectively detecting
contemporary AI-generated images and advocate for a comprehensive reevaluation
of these methods. We introduce the Visual Counter Turing Test (VCT^2), a
benchmark comprising ~130K images generated by contemporary text-to-image
models (Stable Diffusion 2.1, Stable Diffusion XL, Stable Diffusion 3, DALL-E
3, and Midjourney 6). VCT^2 includes two sets of prompts sourced from tweets by
the New York Times Twitter account and captions from the MS COCO dataset. We
also evaluate the performance of the aforementioned AGID techniques on the
VCT^2 benchmark, highlighting their ineffectiveness in detecting AI-generated
images. As image-generative AI models continue to evolve, the need for a
quantifiable framework to evaluate these models becomes increasingly critical.
To meet this need, we propose the Visual AI Index (V_AI), which assesses
generated images from various visual perspectives, including texture complexity
and object coherence, setting a new standard for evaluating image-generative AI
models. To foster research in this domain, we make our
https://huggingface.co/datasets/anonymous1233/COCO_AI and
https://huggingface.co/datasets/anonymous1233/twitter_AI datasets publicly
available.
Current AI-generated image detection techniques are insufficient for identifying modern AI-generated images, prompting the introduction of the Visual Counter Turing Test and Visual AI Index for better evaluation.
AID CNNDetection NPR DM Image Detection Fake Image Detection DIRE LASTED GAN Image Detection AIDE SSP DRCT RINE OCC-CLIP De-Fake Deep Fake Detection Visual Counter Turing Test Visual AI Index texture complexity object coherence