MS-UFAD: A Large-Scale Dataset for Real-world Unified Face Attack Detection with Text Descriptions

Ning Jiang, Dingheng Zeng, Liang Gao, Sheng Chen, Zhifei Kong, Yanhong Liu, Jiao Li, Yue Feng, Tongtong Yuan, Weihong Deng, Quan Lu, Ying Li

Abstract: As deepfake and adversarial attacks evolve, facial recognition systems are encountering increasingly diverse threats. Most existing face liveness detection algorithms focus on single tasks, like spoofing or deepfake attack detection. The corresponding datasets have limited coverage of attack methods, with original data mostly sourced from the internet or laboratory environments. Moreover, existing datasets lack textual annotations, particularly for attack clues, limiting algorithms’ ability to utilize semantic assistance from text. To address these issues, we propose a large-scale unified attack dataset, which includes newly collected facial videos from 5,000 individuals, along with generated videos corresponding to 52 face attack methods. The dataset contains 795k videos and 60k images across four different quality levels. Through semi-automated annotation, we provide detailed textual descriptions. This is the first face attack dataset with textual descriptions. Additionally, we propose a text-guided face attack detection method, demonstrating significant improvements in accuracy using fine-grained textual descriptions. Our dataset will be released at https://ms-ufad.github.io.