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This paper presents a summary of the Competition on Face Morphing AttackDetection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the2022 International Joint Conference on Biometrics (IJCB 2022). The competitionattracted a total of 12 participating teams, both from academia and industryand present in 11 different countries. In the end, seven valid submissions weresubmitted by the participating teams and evaluated by the organizers. Thecompetition was held to present and attract solutions that deal with detectingface morphing attacks while protecting people's privacy for ethical and legalreasons. To ensure this, the training data was limited to synthetic dataprovided by the organizers. The submitted solutions presented innovations thatled to outperforming the considered baseline in many experimental settings. Theevaluation benchmark is now available at:https://github.com/marcohuber/SYN-MAD-2022.

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