Summary of The Adversarial Ai-art: Understanding, Generation, Detection, and Benchmarking, by Yuying Li et al.
The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking
by Yuying Li, Zeyan Liu, Junyi Zhao, Liangqin Ren, Fengjun Li, Jiebo Luo, Bo Luo
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses security concerns surrounding generative AI models that produce high-quality images based on text prompts. The generated images often appear indistinguishable from real images, raising concerns about their potential misuse in fraud, misinformation, or fabricated artworks. To address these concerns, the authors present a systematic attempt to understand and detect AI-generated images (AI-art) in adversarial scenarios. They collect and share a dataset of real images and their corresponding artificial counterparts generated by four popular AI image generators, dubbed ARIA. The dataset contains over 140K images across five categories: artworks, social media images, news photos, disaster scenes, and anime pictures. Additionally, the authors conduct a user study to evaluate if real-world users can distinguish between AI-art and real images with or without reference images. They also benchmark state-of-the-art open-source and commercial AI image detectors on the ARIA dataset. Finally, they present a ResNet-50 classifier and evaluate its accuracy and transferability on the same dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about keeping track of fake images made by artificial intelligence (AI) that look very real. These AI-generated images can be used to trick people or spread misinformation. The authors want to know if it’s possible to tell if an image was created by a human or AI. They collected many examples of both types of images and asked people to compare them. They also tested different computer programs designed to detect fake images. By doing this research, they hope to help keep the internet safe from fake images. |
Keywords
» Artificial intelligence » Resnet » Transferability
