Summary of Ru-ai: a Large Multimodal Dataset For Machine-generated Content Detection, by Liting Huang et al.
RU-AI: A Large Multimodal Dataset for Machine-Generated Content Detection
by Liting Huang, Zhihao Zhang, Yiran Zhang, Xiyue Zhou, Shoujin Wang
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 recent advancements in generative AI models have revolutionized the way people communicate, create, and work. These machine-generated contents can be both beneficial and detrimental to society, posing a threat of misleading information when mixed with natural human content. To address this challenge, it is essential to develop effective methods for detecting machine-generated content. However, the lack of aligned multimodal datasets has hindered the development of such methods, particularly in triple-modality settings like text, image, and voice. This paper introduces RU-AI, a large-scale multimodal dataset for robust and effective detection of machine-generated content in these three modalities. The dataset is constructed using publicly available datasets like Flickr8K, COCO, and Places205, with AI duplicates added to create 1,475,370 instances. Additionally, an extra noise variant was created for testing the robustness of detection models. Experimental results show that existing SOTA detection methods struggle to achieve accurate and robust detection on this dataset. The aim is to promote research in machine-generated content detection, fostering responsible use of generative AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are getting very good at creating fake content that looks real. This can be both helpful and harmful. For example, it could help people by making tasks easier, but it could also trick people into thinking something is true when it’s not. To make sure we’re using these models responsibly, we need to find ways to detect when the content was created by a machine rather than a person. The problem is that there aren’t many datasets that have both real and fake content in different formats like text, images, and voice. This paper creates a big dataset called RU-AI that has 1.4 million examples of real and fake content. They also added some extra noisy data to test how well the detection models work. The results show that current methods aren’t very good at detecting fake content in this new dataset. |
Keywords
» Artificial intelligence » Machine learning
