Visual Counter Turing Test (VCT^2): Discovering the Challenges for AI-Generated Image Detection and Introducing Visual AI Index (V_AI)
- URL: http://arxiv.org/abs/2411.16754v1
- Date: Sun, 24 Nov 2024 06:03:49 GMT
- Title: Visual Counter Turing Test (VCT^2): Discovering the Challenges for AI-Generated Image Detection and Introducing Visual AI Index (V_AI)
- Authors: Nasrin Imanpour, Shashwat Bajpai, Subhankar Ghosh, Sainath Reddy Sankepally, Abhilekh Borah, Hasnat Md Abdullah, Nishoak Kosaraju, Shreyas Dixit, Ashhar Aziz, Shwetangshu Biswas, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das,
- Abstract summary: 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.
We introduce the Visual Counter Turing Test (VCT2), a benchmark comprising 130K images generated by text-to-image models.
We also evaluate the performance of the aforementioned AGID techniques on the VCT$2$ benchmark, highlighting their ineffectiveness in detecting AI-generated
- Score: 5.8695051911828555
- License:
- Abstract: 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.
Related papers
- Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors [62.63467652611788]
We introduce SEMI-TRUTHS, featuring 27,600 real images, 223,400 masks, and 1,472,700 AI-augmented images.
Each augmented image is accompanied by metadata for standardized and targeted evaluation of detector robustness.
Our findings suggest that state-of-the-art detectors exhibit varying sensitivities to the types and degrees of perturbations, data distributions, and augmentation methods used.
arXiv Detail & Related papers (2024-11-12T01:17:27Z) - Zero-Shot Detection of AI-Generated Images [54.01282123570917]
We propose a zero-shot entropy-based detector (ZED) to detect AI-generated images.
Inspired by recent works on machine-generated text detection, our idea is to measure how surprising the image under analysis is compared to a model of real images.
ZED achieves an average improvement of more than 3% over the SoTA in terms of accuracy.
arXiv Detail & Related papers (2024-09-24T08:46:13Z) - A Sanity Check for AI-generated Image Detection [49.08585395873425]
We present a sanity check on whether the task of AI-generated image detection has been solved.
To quantify the generalization of existing methods, we evaluate 9 off-the-shelf AI-generated image detectors on Chameleon dataset.
We propose AIDE (AI-generated Image DEtector with Hybrid Features), which leverages multiple experts to simultaneously extract visual artifacts and noise patterns.
arXiv Detail & Related papers (2024-06-27T17:59:49Z) - Improving Interpretability and Robustness for the Detection of AI-Generated Images [6.116075037154215]
We analyze existing state-of-the-art AIGI detection methods based on frozen CLIP embeddings.
We show how to interpret them, shedding light on how images produced by various AI generators differ from real ones.
arXiv Detail & Related papers (2024-06-21T10:33:09Z) - Development of a Dual-Input Neural Model for Detecting AI-Generated Imagery [0.0]
It is important to develop tools that are able to detect AI-generated images.
This paper proposes a dual-branch neural network architecture that takes both images and their Fourier frequency decomposition as inputs.
Our proposed model achieves an accuracy of 94% on the CIFAKE dataset, which significantly outperforms classic ML methods and CNNs.
arXiv Detail & Related papers (2024-06-19T16:42:04Z) - RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection [60.960988614701414]
RIGID is a training-free and model-agnostic method for robust AI-generated image detection.
RIGID significantly outperforms existing trainingbased and training-free detectors.
arXiv Detail & Related papers (2024-05-30T14:49:54Z) - AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images [70.42666704072964]
We establish a large-scale AI generated omnidirectional image IQA database named AIGCOIQA2024.
A subjective IQA experiment is conducted to assess human visual preferences from three perspectives.
We conduct a benchmark experiment to evaluate the performance of state-of-the-art IQA models on our database.
arXiv Detail & Related papers (2024-04-01T10:08:23Z) - Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images [67.18010640829682]
We show that AI-generated images introduce an invisible relevance bias to text-image retrieval models.
The inclusion of AI-generated images in the training data of the retrieval models exacerbates the invisible relevance bias.
We propose an effective training method aimed at alleviating the invisible relevance bias.
arXiv Detail & Related papers (2023-11-23T16:22:58Z) - AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors [24.78672820633581]
Deep generative models can create remarkably fake images while raising concerns about misinformation and copyright infringement.
Deepfake detection technique is developed to distinguish between real and fake images.
We propose a novel approach called AntifakePrompt, using Vision-Language Models and prompt tuning techniques.
arXiv Detail & Related papers (2023-10-26T14:23:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.