Hidetsugu Uchida, Narishige Abe and Shigefumi YamadaBrömme, ArslanDamer, NaserGomez-Barrero, MartaRaja, KiranRathgeb, ChristianSequeira Ana F.Todisco, MassimilianoUhl, Andreas2022-10-272022-10-272022978-3-88579-723-4https://dl.gi.de/handle/20.500.12116/39717As a countermeasure against malicious authentication in a face recognition system using a face image obtained from SNS or the like, de-identification methods based on adversarial example have been studied. However, since adversarial example directly uses the gradient information of a face recognition model, it is highly dependent on the model, and a de-identification effect and image quality are difficult to achieve for an unknown recognition model. In this study, we propose a novel de-identification method based on a diffusion model, which has high generalizability to an unknown recognition model by applying minute changes to face shapes. Experiments using LFW showed that the proposed method has a higher de-identification effect for unknown models and better image quality than a conventional method using adversarial example.enFace recognitionde-identificationdiffusion modelText/Conference Paper10.1109/BIOSIG55365.2022.98969721617-5474