ANID: How Far Are We? Evaluating the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance

• URL: http://arxiv.org/abs/2412.17632v1
• Date: Mon, 23 Dec 2024 15:08:08 GMT
• Title: ANID: How Far Are We? Evaluating the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance
• Authors: Renyang Liu, Ziyu Lyu, Wei Zhou, See-Kiong Ng,
• Abstract summary: We introduce an AI-Natural Image Discrepancy Evaluation benchmark aimed at addressing the critical question: textithow far are AI-generated images from truly realistic images? We have constructed a large-scale multimodal dataset, the Distinguishing Natural and AI-generated Images (DNAI) dataset, which includes over 440,000 AIGI samples generated by 8 representative models. Our fine-grained assessment framework provides a comprehensive evaluation of the DNAI dataset across five key dimensions.
• Score: 19.760989919485894
• License:
• Abstract: In the rapidly evolving field of Artificial Intelligence Generated Content (AIGC), one of the key challenges is distinguishing AI-synthesized images from natural images. Despite the remarkable capabilities of advanced AI generative models in producing visually compelling images, significant discrepancies remain when these images are compared to natural ones. To systematically investigate and quantify these discrepancies, we introduce an AI-Natural Image Discrepancy Evaluation benchmark aimed at addressing the critical question: \textit{how far are AI-generated images (AIGIs) from truly realistic images?} We have constructed a large-scale multimodal dataset, the Distinguishing Natural and AI-generated Images (DNAI) dataset, which includes over 440,000 AIGI samples generated by 8 representative models using both unimodal and multimodal prompts, such as Text-to-Image (T2I), Image-to-Image (I2I), and Text \textit{vs.} Image-to-Image (TI2I). Our fine-grained assessment framework provides a comprehensive evaluation of the DNAI dataset across five key dimensions: naive visual feature quality, semantic alignment in multimodal generation, aesthetic appeal, downstream task applicability, and coordinated human validation. Extensive evaluation results highlight significant discrepancies across these dimensions, underscoring the necessity of aligning quantitative metrics with human judgment to achieve a holistic understanding of AI-generated image quality. Code is available at \href{https://github.com/ryliu68/ANID}{https://github.com/ryliu68/ANID}.

Related papers

• AI-generated Image Quality Assessment in Visual Communication [72.11144790293086]
  AIGI-VC is a quality assessment database for AI-generated images in visual communication. The dataset consists of 2,500 images spanning 14 advertisement topics and 8 emotion types. It provides coarse-grained human preference annotations and fine-grained preference descriptions, benchmarking the abilities of IQA methods in preference prediction, interpretation, and reasoning.
  arXiv  Detail & Related papers  (2024-12-20T08:47:07Z)
• Visual Counter Turing Test (VCT^2): Discovering the Challenges for AI-Generated Image Detection and Introducing Visual AI Index (V_AI) [5.8695051911828555]
  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
  arXiv  Detail & Related papers  (2024-11-24T06:03:49Z)
• A Sanity Check for AI-generated Image Detection [49.08585395873425]
  We propose AIDE (AI-generated Image DEtector with Hybrid Features) to detect AI-generated images. AIDE achieves +3.5% and +4.6% improvements to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-27T17:59:49Z)
• 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)
• Large Multi-modality Model Assisted AI-Generated Image Quality Assessment [53.182136445844904]
  We introduce a large Multi-modality model Assisted AI-Generated Image Quality Assessment (MA-AGIQA) model. It uses semantically informed guidance to sense semantic information and extract semantic vectors through carefully designed text prompts. It achieves state-of-the-art performance, and demonstrates its superior generalization capabilities on assessing the quality of AI-generated images.
  arXiv  Detail & Related papers  (2024-04-27T02:40:36Z)
• 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)
• PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images [1.6031185986328562]
  We establish a human perception-based image-to-image AIGCIQA database, named PKU-I2IQA. We propose two benchmark models: NR-AIGCIQA based on the no-reference image quality assessment method and FR-AIGCIQA based on the full-reference image quality assessment method.
  arXiv  Detail & Related papers  (2023-11-27T05:53:03Z)
• 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)
• CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images [7.868449549351487]
  This article proposes to enhance our ability to recognise AI-generated images through computer vision. The two sets of data present as a binary classification problem with regard to whether the photograph is real or generated by AI. This study proposes the use of a Convolutional Neural Network (CNN) to classify the images into two categories; Real or Fake.
  arXiv  Detail & Related papers  (2023-03-24T16:33:06Z)

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.