Second Competition on Presentation Attack Detection on ID Card

• URL: http://arxiv.org/abs/2507.20404v1
• Date: Sun, 27 Jul 2025 20:18:52 GMT
• Title: Second Competition on Presentation Attack Detection on ID Card
• Authors: Juan E. Tapia, Mario Nieto, Juan M. Espin, Alvaro S. Rocamora, Javier Barrachina, Naser Damer, Christoph Busch, Marija Ivanovska, Leon Todorov, Renat Khizbullin, Lazar Lazarevich, Aleksei Grishin, Daniel Schulz, Sebastian Gonzalez, Amir Mohammadi, Ketan Kotwal, Sebastien Marcel, Raghavendra Mudgalgundurao, Kiran Raja, Patrick Schuch, Sushrut Patwardhan, Raghavendra Ramachandra, Pedro Couto Pereira, Joao Ribeiro Pinto, Mariana Xavier, Andrés Valenzuela, Rodrigo Lara, Borut Batagelj, Marko Peterlin, Peter Peer, Ajnas Muhammed, Diogo Nunes, Nuno Gonçalves,
• Abstract summary: 20 teams were registered, and 74 submitted models were evaluated.<n>For Track 1, the "Dragons" team reached first place with an Average Ranking and Equal Error rate (EER) of AV-Rank of 40.48% and 11.44% EER, respectively.<n>For the more challenging approach in Track 2, the "Incode" team reached the best results with an AV-Rank of 14.76% and 6.36% EER, improving on the results of the first edition of 74.30% and 21.87% EER, respectively.
• Score: 12.609487726543774
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This work summarises and reports the results of the second Presentation Attack Detection competition on ID cards. This new version includes new elements compared to the previous one. (1) An automatic evaluation platform was enabled for automatic benchmarking; (2) Two tracks were proposed in order to evaluate algorithms and datasets, respectively; and (3) A new ID card dataset was shared with Track 1 teams to serve as the baseline dataset for the training and optimisation. The Hochschule Darmstadt, Fraunhofer-IGD, and Facephi company jointly organised this challenge. 20 teams were registered, and 74 submitted models were evaluated. For Track 1, the "Dragons" team reached first place with an Average Ranking and Equal Error rate (EER) of AV-Rank of 40.48% and 11.44% EER, respectively. For the more challenging approach in Track 2, the "Incode" team reached the best results with an AV-Rank of 14.76% and 6.36% EER, improving on the results of the first edition of 74.30% and 21.87% EER, respectively. These results suggest that PAD on ID cards is improving, but it is still a challenging problem related to the number of images, especially of bona fide images.

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