CONF Tapia_IJCB2025_2025/IDIAP Second Competition on Presentation Attack Detection on ID Card Tapia, Juan E. Mario, Nieto Espin, Juan Sanchez, Alvaro Damer, Naser Busch, Christoph Ivanovska, Marija Todorov, Leon Khizbullin, Renat Grishin, Aleksei Lazarevic, Lazar Schulz, Daniel Gonzalez, Sebastian Mohammadi, Amir Kotwal, Ketan Marcel, Sébastien Mudgalgundurao, Raghavendra Raja, Kiran B. Schuch, Patrick Couto, Pedro Pinto, Joao Xavier, Mariana Valenzuela, Andres Batagelj, Borut Barrachina, Javier Peterlin, Marko Peer, Peter Muhammed, Ajnas Nunes, Diogo Gonçalves, Nuno Patwardhan, Sushrut Ramachandra, Raghavendra EXTERNAL https://publications.idiap.ch/attachments/papers/2025/Tapia_IJCB2025_2025.pdf PUBLIC IEEE International Joint Conference on Biometrics (IJCB) 2025 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 organised jointly 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.