D-Judge: How Far Are We? Assessing the Discrepancies Between AI-synthesized and Natural Images through Multimodal Guidance
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915438669070336 |
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| author | Liu, Renyang Lyu, Ziyu Zhou, Wei Ng, See-Kiong |
| author_facet | Liu, Renyang Lyu, Ziyu Zhou, Wei Ng, See-Kiong |
| contents | In the rapidly evolving field of Artificial Intelligence Generated Content (AIGC), a central challenge is distinguishing AI-synthesized images from natural ones. Despite the impressive capabilities of advanced generative models in producing visually compelling images, significant discrepancies remain when compared to natural images. To systematically investigate and quantify these differences, we construct a large-scale multimodal dataset, D-ANI, comprising 5,000 natural images and over 440,000 AIGI samples generated by nine representative models using both unimodal and multimodal prompts, including Text-to-Image (T2I), Image-to-Image (I2I), and Text-and-Image-to-Image (TI2I). We then introduce an AI-Natural Image Discrepancy assessment benchmark (D-Judge) to address the critical question: how far are AI-generated images (AIGIs) from truly realistic images? Our fine-grained evaluation framework assesses the D-ANI dataset across five dimensions: naive visual quality, semantic alignment, aesthetic appeal, downstream task applicability, and coordinated human validation. Extensive experiments reveal substantial discrepancies across these dimensions, highlighting the importance of aligning quantitative metrics with human judgment to achieve a comprehensive understanding of AI-generated image quality. Code: https://github.com/ryliu68/DJudge ; Data: https://huggingface.co/datasets/Renyang/DANI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17632 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | D-Judge: How Far Are We? Assessing the Discrepancies Between AI-synthesized and Natural Images through Multimodal Guidance Liu, Renyang Lyu, Ziyu Zhou, Wei Ng, See-Kiong Artificial Intelligence Computer Vision and Pattern Recognition Multimedia In the rapidly evolving field of Artificial Intelligence Generated Content (AIGC), a central challenge is distinguishing AI-synthesized images from natural ones. Despite the impressive capabilities of advanced generative models in producing visually compelling images, significant discrepancies remain when compared to natural images. To systematically investigate and quantify these differences, we construct a large-scale multimodal dataset, D-ANI, comprising 5,000 natural images and over 440,000 AIGI samples generated by nine representative models using both unimodal and multimodal prompts, including Text-to-Image (T2I), Image-to-Image (I2I), and Text-and-Image-to-Image (TI2I). We then introduce an AI-Natural Image Discrepancy assessment benchmark (D-Judge) to address the critical question: how far are AI-generated images (AIGIs) from truly realistic images? Our fine-grained evaluation framework assesses the D-ANI dataset across five dimensions: naive visual quality, semantic alignment, aesthetic appeal, downstream task applicability, and coordinated human validation. Extensive experiments reveal substantial discrepancies across these dimensions, highlighting the importance of aligning quantitative metrics with human judgment to achieve a comprehensive understanding of AI-generated image quality. Code: https://github.com/ryliu68/DJudge ; Data: https://huggingface.co/datasets/Renyang/DANI. |
| title | D-Judge: How Far Are We? Assessing the Discrepancies Between AI-synthesized and Natural Images through Multimodal Guidance |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2412.17632 |