RU-AI: A Large Multimodal Dataset for Machine-Generated Content Detection

The recent generative AI models' capability of creating realistic and human-like content is significantly transforming the ways in which people communicate, create and work. The appropriate use of generative AI models can benefit society, while their misuse poses threats to the society. However...

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Vydáno v:arXiv.org
Hlavní autoři: Huang, Liting, Zhang, Zhihao, Zhang, Yiran, Zhou, Xiyue, Wang, Shoujin
Médium: Paper
Jazyk:angličtina
Vydáno: Ithaca Cornell University Library, arXiv.org 19.12.2024
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Datasets
Generative artificial intelligence
Multilayer perceptrons
Robustness
Source code
ISSN:2331-8422
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Shrnutí:The recent generative AI models' capability of creating realistic and human-like content is significantly transforming the ways in which people communicate, create and work. The appropriate use of generative AI models can benefit society, while their misuse poses threats to the society. However, the lack of aligned multimodal datasets has inhibited the development of effective and robust methods for detecting machine-generated content, particularly in triple-modality settings (e.g., text, image, and voice). In this paper, we introduce RU-AI, a new large-scale multimodal dataset for robust and efficient detection of machine-generated content in text, image and voice. Our dataset is constructed on the basis of three large publicly available datasets: Flickr8K, COCO and Places205, by adding their corresponding AI duplicates, resulting total of 1,475,370 data instances. In addition, we create a noise variant of each modality of the datasets aiming to analyse the models' robustness. Given our dataset, we conduct extensive experiments with the current SOTA detection methods. The results reveal that existing models still struggle to achieve accurate and robust classification after training on our dataset. The RU-AI dataset is designed to support the development of detection methods across modalities and can be effectively utilised for identifying machine-generated content. The source code and dataset are available at https://github.com/ZhihaoZhang97/RU-AI.
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ISSN:2331-8422