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Author implementation of RiDDLE: Reversible and Diversified De-identification with Latent Encryptor (CVPR 2023)

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RiDDLE (CVPR 2023)

RiDDLE

Author implementation of RiDDLE: Reversible and Diversified De-identification with Latent Encryptor

Dongze Li, Wei Wang, Kang Zhao, Jing Dong and Tieniu Tan

Abstract

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.

Pipeline

RiDDLE_pipeline

Usage

Environment

To run the inference and the training scripts, first you need to set up a virtual environment by conda env create -f RiDDLE.yaml

Inference

To perform identity encryption and decryption, just python coach_test.py

Train

To train a latent encryptor, just sh scripts/run_coach_id_pwd_same.sh

We also support Distributed Data Parallel (DDP) training, sh scripts/run_coach_id_pwd_same_ddp.sh.

Data and Pretrain models

Our data and pretrain models can be found at this link, password is sqp8

Video De-identification Results

We combine our method with Stitch Tuning to de-identify videos.

Curry

curry_10s.mov

Lebron

lebron_10s.mov

Jim

jim.mov

Citation

If you found this repo useful, please cite

@inproceedings{li2023riddle,
  title={RiDDLE: Reversible and Diversified De-identification with Latent Encryptor},
  author={Li, Dongze and Wang, Wei and Zhao, Kang and Dong, Jing and Tan, Tieniu},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month={June},
  year={2023}
}

Acknowledgements

Some parts of our code is based on HairCLIP and e4e, thanks for their great work.

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Author implementation of RiDDLE: Reversible and Diversified De-identification with Latent Encryptor (CVPR 2023)

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