Biometric face morph attack detection breakthroughs offer border security hope Morphing attack detection (MAD) was a major theme of the European Association for Biometrics’ (EAB’s) workshop on live enrollment last year, and as AI makes sophisticated biometric spoof attacks more inexpensive and widely available, the subject has graduated to a full multi-day examination. Dr. Annalisa Franco of Italy’s University of Bologna hosted the first half of a two-day workshop on the state of the art and outlook for biometric face morphing on Tuesday. Morphs and facial recognition improve with training, but not border guards Face morphing attack image creation has evolved from landmark warping to diffusion synthesis, Kiran Raja of the Norwegian University of Science and Technology (NTNU) explained to open the workshop. Landmark-based approaches using Delaunay Triangulation and Affine transformations can leave behind artifacts that are visible on close inspection. Post-processing can clean these up, but only to a certain extent. GANs have similar drawbacks, but Raja and his research associates have found that attackers can use diffusion models to generate attack images that do not have the same flaws. Diffusion morphs are not perfect, but they are the most difficult to detect, reaching up to 99.8 percent in Raja’s tests, and therefore should be included in training data. Raja went on to describe different morph attack image creation techniques and their relative effectiveness. David Robertson of the University of Strathclyde in Scotland introduced the concept of familiar or unfamiliar face recognition. These terms describe how people can identify the same person in widely varying photos, if they know them, and yet fail at the much simpler task of matching a person standing in front of them to, or differentiating them from, a photo. He also shared the results of a study which showed that training people on what