Targeted Anonymization: A Face Image Anonymization Method for Unauthorized Models
Kangyi Ding, Teng Hu, Xiaolei Liu, Weina Niu, Yanping Wang, Xiaosong Zhang · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
As an important biometric feature of every person, face data has faced serious risks of leakage in recent years. Lawbreakers can use face recognition systems (FRS) to analyze the leaked face data and then correlate other private information, causing serious privacy leaks. For security reasons, we hope our face images can only be recognized by the organizations' authorized models. To achieve this goal, this work proposes a targeted face image anonymization method that only enables anonymization for unauthorized facial recognition models, whilst authorized models, human eyes can still accurately recognize faces. Our method mainly uses transfer-based adversarial attacks to achieve anonymization. On this basis, we propose constraints for generating targeted anonymization samples and boundary walking strategy, focusing on improving the anonymization for unauthorized models while guaranteeing the accurate recognition of authorized models. Local experiments prove that our method can reduce the recognition probability of unauthorized models while guaranteeing the correctness of authorized models. Finally, we apply our approach to an online face recognition API and experimentally demonstrate that our approach can significantly reduce the recognition accuracy of the commercial face recognition model.