MVAD: A Benchmark Dataset for Multimodal AI-Generated Video-Audio Detection
This dataset fills a critical gap for researchers developing detection systems for general multimodal AI-generated content, moving beyond the limited scope of facial deepfakes.
The paper introduces MVAD, the first comprehensive dataset for detecting AI-generated multimodal video-audio content, addressing the lack of general multimodal forgeries beyond facial deepfakes. The dataset includes three realistic forgery patterns, high perceptual quality, and diverse styles and categories.
The rapid advancement of AI-generated multimodal video-audio content has raised significant concerns regarding information security and content authenticity. Existing synthetic video datasets predominantly focus on the visual modality alone, while the few incorporating audio are largely confined to facial deepfakes--a limitation that fails to address the expanding landscape of general multimodal AI-generated content and substantially impedes the development of trustworthy detection systems. To bridge this critical gap, we introduce the Multimodal Video-Audio Dataset (MVAD), the first comprehensive dataset specifically designed for detecting AI-generated multimodal video-audio content. Our dataset exhibits three key characteristics: (1) genuine multimodality with samples generated according to three realistic video-audio forgery patterns; (2) high perceptual quality achieved through diverse state-of-the-art generative models; and (3) comprehensive diversity spanning realistic and anime visual styles, four content categories (humans, animals, objects, and scenes), and four video-audio multimodal data types. Our dataset will be available at https://github.com/HuMengXue0104/MVAD.