This work presents RiDDLE, short for Reversible and
Diversified De-identification with Latent Encryptor, to pro-
tect 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 de-
crypted 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 is
available in https://github.com/ldz666666/RiDDLE.