Summary of Anid: How Far Are We? Evaluating the Discrepancies Between Ai-synthesized Images and Natural Images Through Multimodal Guidance, by Renyang Liu et al.
ANID: How Far Are We? Evaluating the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance
by Renyang Liu, Ziyu Lyu, Wei Zhou, See-Kiong Ng
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses a critical challenge in Artificial Intelligence Generated Content (AIGC), which is distinguishing AI-synthesized images from natural ones. The authors introduce an evaluation benchmark, the AI-Natural Image Discrepancy Evaluation (ANIDE) dataset, to quantify the discrepancies between AI-generated and realistic images. The ANIDE dataset comprises over 440,000 AI-generated image samples, generated by 8 representative models using various prompts. A fine-grained assessment framework evaluates the DNAI dataset across five key dimensions: visual feature quality, semantic alignment, aesthetic appeal, downstream task applicability, and human validation. Results highlight significant discrepancies across these dimensions, underscoring the importance of aligning quantitative metrics with human judgment to understand AI-generated image quality. The authors provide code at https://github.com/ryliu68/ANID for this benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out how to tell apart fake images made by computers from real ones. Right now, it’s hard to do this because AI-generated images can look very realistic. The researchers created a big dataset with many fake images and asked experts to evaluate them. They found that there are some big differences between the fake images and real ones. This is important because we need to be able to tell what’s real and what’s not. The code for this project is available online. |
Keywords
» Artificial intelligence » Alignment
