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Secur."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Privacy regulations and ethical concerns have encouraged the use of privacy-friendly synthetic data for the training of facial analysis systems. However, the automated generation of images depicting the same synthetic subject in different environmental scenarios remains challenging, as identity-related features may not be accurately preserved. This is a severe issue for the training of differential morphing attack detection (MAD) algorithms, where subtle differences in facial features can indicate morphing attacks. This work introduces IDSwapMAD as a new way for generating privacy-friendly training data for differential MAD methods. In detail, a generative adversarial network is employed to generate synthetic facial images of which the faces are swapped with pairs of real reference and probe images containing variations that mimic a border control scenario. In this way, style-related properties of the reference and probe images are retained, while identity-related features are replaced. It is shown that the proposed IDSwapMAD technique is an effective and privacy-friendly strategy for training differential MAD methods, whose detection performance is on par with a state-of-the-art MAD method trained on real data.<\/jats:p>","DOI":"10.1186\/s13635-026-00227-9","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T07:36:13Z","timestamp":1770622573000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IDSwapMAD: towards privacy-friendly training of differential face morphing attack detection"],"prefix":"10.1186","volume":"2026","author":[{"given":"Adrian","family":"Banas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Rathgeb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"Merkle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maxim","family":"Schaubert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"227_CR1","doi-asserted-by":"publisher","first-page":"23012","DOI":"10.1109\/ACCESS.2019.2899367","volume":"7","author":"U Scherhag","year":"2019","unstructured":"U. 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