title: Visual Counter Turing Test (VCT^2): Discovering the Challenges for
    AI-Generated Image Detection and Introducing Visual AI Index (V_AI)
summary: 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.
paper_authors._id: 6746a7c80a7a7bc04551f5c0
paper_authors.name: Nasrin Imanpour
paper_authors._id: 6746a7c80a7a7bc04551f5c1
paper_authors.name: Shashwat Bajpai
paper_authors.status: claimed_verified
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paper_authors.fullname: Shashwat Bajpai
paper_authors.type: user
paper_authors.user: Shashwat1729
paper_authors._id: 6746a7c80a7a7bc04551f5c2
paper_authors.name: Subhankar Ghosh
paper_authors._id: 6746a7c80a7a7bc04551f5c3
paper_authors.name: Sainath Reddy Sankepally
paper_authors._id: 6746a7c80a7a7bc04551f5c4
paper_authors.name: Abhilekh Borah
paper_authors._id: 6746a7c80a7a7bc04551f5c5
paper_authors.name: Hasnat Md Abdullah
paper_authors._id: 6746a7c80a7a7bc04551f5c6
paper_authors.name: Nishoak Kosaraju
paper_authors._id: 6746a7c80a7a7bc04551f5c7
paper_authors.name: Shreyas Dixit
paper_authors._id: 6746a7c80a7a7bc04551f5c8
paper_authors.name: Ashhar Aziz
paper_authors._id: 6746a7c80a7a7bc04551f5c9
paper_authors.name: Shwetangshu Biswas
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-02T20:19:28.742Z
paper_authors._id: 65aab5ee14d782df06e92037
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/kCr0jRi6X_Bhgw7Tfs5qj.jpeg
paper_authors.fullname: Shwetangshu Biswas
paper_authors.type: user
paper_authors.user: shwetangshub
paper_authors._id: 6746a7c80a7a7bc04551f5ca
paper_authors.name: Vinija Jain
paper_authors._id: 6746a7c80a7a7bc04551f5cb
paper_authors.name: Aman Chadha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-27T22:20:00.163Z
paper_authors._id: 63a4754927f1f64ed7238dac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_authors._id: 6746a7c80a7a7bc04551f5cc
paper_authors.name: Amit Sheth
paper_authors._id: 6746a7c80a7a7bc04551f5cd
paper_authors.name: Amitava Das
paper_title: Visual Counter Turing Test (VCT^2): Discovering the Challenges for
    AI-Generated Image Detection and Introducing Visual AI Index (V_AI)
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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