To benchmark face anti-spoofing methods specifically for unknown attacks, we collect the Spoof in the Wild database with Multiple Attack Types (SiW-M). SiW-M shows a great diversity in spoof attacks, subject identities, environments and other factors. If you are looking for dataset for replay/print attacks only, click here to visit SiW database.

Database Discrption

SiW-M provides live and spoof videos from 493 subjects. Overall, we collect 660 live and 968 spoof videos of 13 types of spoof attacks listed hieratically in Fig 1, in total 1,628 videos. The live videos are collected in 3 sessions: 1) a room environment where the subjects are recorded with few variations such as pose, lighting and expression (PIE). 2) a different and much larger room where the subjects are also recorded with PIE variations. 3) a mobile phone mode, where the subjects are moving while the phone camera is recording. Extreme pose angles and lighting conditions are introduced. For all 5 mask attacks, 3 partial attacks, obfuscation makeup and cosmetic makeup, we record 1080P HD videos. For impersonation makeup, we collect 720P videos from Youtube due to the lack of special makeup artists. For print and replay attacks, we intend to collect videos from harder cases where the existing system fails. The spoof videos are collected with several attacks such as printed paper and replay. More details are in Section 4.

Overview Example

Figure 1: The statistics and attack types of SiW-M database.

Evaluation Protocols

To set a baseline for future study on SiW-M, we define the leave-one-out protocols for SiW-M. In the following figure, we present baseline performance.


Figure 3: The baseline performance on three protocols of SiW.

How To Download

1. SiW-M database is available under a license from Michigan State University for research purposes. Please download and sign this Dataset Release Agreement (DRA).

2. Submit the request and upload your signed DRA at Online Application.

3. If you are unable to access Google Server in Step 2, please send an email to with the following information:

    • Title: SiW-M Application
    • CC: Your advisor's email
    • Content Line 1: Your name, email, affiliation
    • Content Line 2: Your advisor's name, email, webpage
    • Attachment: Signed DRA
    • (Wrong formats may cost longer time to process!)

4. You will receive the download username/password and instructions upon approval of your usage of the database, and you can download SiW-M database within 30 days from approval.


  • Deep Tree Learning for Zero-Shot Face Anti-Spoofing
    Yaojie Liu, Joel Stehouwer, Amin Jourabloo, Xiaoming Liu
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, Jun. 2019
    Bibtex | PDF | arXiv | Project Webpage
  • @inproceedings{ deep-tree-learning-for-zero-shot-face-anti-spoofing,
      author = { Yaojie Liu and Joel Stehouwer and Amin Jourabloo and Xiaoming Liu },
      title = { Deep Tree Learning for Zero-Shot Face Anti-Spoofing },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Long Beach, CA },
      month = { June },
      year = { 2019 },