Summary of Asap: Interpretable Analysis and Summarization Of Ai-generated Image Patterns at Scale, by Jinbin Huang et al.
ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale
by Jinbin Huang, Chen Chen, Aditi Mishra, Bum Chul Kwon, Zhicheng Liu, Chris Bryan
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 abstract proposes an interactive visualization system called ASAP that enables users to effectively discern and comprehend patterns of AI-generated images. The system employs a novel image encoder adapted from CLIP, which transforms images into compact representations enriched with information for differentiating authentic and fake images. ASAP’s unique features include multiple coordinated visualizations, including a representation overview and pattern view, allowing users to analyze cutting-edge generative models like GAN-based proGAN and diffusion models like latent diffusion model. The system is demonstrated through two usage scenarios using multiple fake image detection benchmark datasets, revealing its ability to identify hidden patterns in AI-generated images, particularly detecting fake human faces produced by diffusion-based techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ASAP is a special tool that helps people understand how AI creates pictures. Right now, there’s concern about AI making fake pictures that could cause problems. ASAP lets users explore and analyze these pictures to spot the differences between real and fake ones. The tool uses a new way of looking at images called CLIP, which helps sort out what’s genuine or not. ASAP shows this information in different ways, like a big picture view and a detailed pattern view. This makes it easy for experts to use ASAP with popular AI image-making tools like proGAN and latent diffusion model. The tool is shown working well in two special tests using real-world datasets. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Encoder » Gan
