SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data

TL;DR

The SYN-MAD 2022 competition focused on developing face morphing attack detection methods using privacy-preserving synthetic training data, attracting global participants and showcasing innovative solutions that outperform baseline methods.

Contribution

This paper summarizes the SYN-MAD 2022 competition, highlighting the use of synthetic data for privacy-aware face morphing attack detection and the participating teams' innovative solutions.

Findings

Participants outperformed baseline methods in various settings.

Synthetic training data effectively supports face morphing attack detection.

The competition fostered global collaboration and innovation.

Abstract

This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and attract solutions that deal with detecting face morphing attacks while protecting people's privacy for ethical and legal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to outperforming the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://github.com/marcohuber/SYN-MAD-2022.