This work presents RiDDLE, short for Reversible and Diversified
De-identification with Latent Encryptor, to protect the identity information of
people from being misused. Built upon a pre-learned StyleGAN2 generator, RiDDLE
manages to encrypt and decrypt the facial identity within the latent space. The
design of RiDDLE has three appealing properties. First, the encryption process
is cipher-guided and hence allows diverse anonymization using different
passwords. Second, the true identity can only be decrypted with the correct
password, otherwise the system will produce another de-identified face to
maintain the privacy. Third, both encryption and decryption share an efficient
implementation, benefiting from a carefully tailored lightweight encryptor.
Comparisons with existing alternatives confirm that our approach accomplishes
the de-identification task with better quality, higher diversity, and stronger
reversibility. We further demonstrate the effectiveness of RiDDLE in
anonymizing videos. Code and models will be made publicly available.

本文使用预先训练的 StyleGAN2 生成器，提出了一种名为 RiDDLE 的加密程序，可以在潜在空间内加密和解密面部身份信息，并具有密码指导的加密等吸引人的特性。该方法通过对比现有方法证实，能够更好地实现去识别任务。