# MAP2V
Validating Privacy-Preserving Face Recognition under a Minimum Assumption To be presented in the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024
The widespread use of cloud-based face recognition technology raises privacy concerns, as unauthorized access to face images can expose personal information or be exploited for fraudulent purposes. In response, privacy-preserving face recognition (PPFR) schemes have emerged to hide visual information and impede recovery. However, their privacy validation methods, built on unrealistic assumptions, raise doubts about proclaimed face privacy preservation capabilities. In this paper, we introduce a general privacy-preserving validation version under the novel concept of minimum assumption coined Map$^2$V. This is the first exploration of formulating a privacy validation method utilizing deep image priors and zeroth-order gradient estimation, with the potential to serve as a generalizable tool for PPFR evaluation. Building upon Map$^2$V, we comprehensively validate the anti-privacy inference capability of PPFRs through a combination of human and machine vision. The results of experiments and analysis demonstrate the effectiveness and generalizability of the proposed Map$^2$V, showcasing its superiority over native privacy validation methods from PPFR works of literature. Additionally, this work exposes privacy vulnerabilities in evaluated state-of-the-art PPFR schemes, laying the foundation for the subsequent effective proposal of countermeasures.
Download pretrained privacy-preserving face recognition model and weights: DuetFace DCTDP PartialFace
[all_modelweights] (https://pan.baidu.com/s/1OmW93OEEXHTxDrH-gk1rw?pwd=2fb4) Fetch Code: 2fb4 or google drive: https://drive.google.com/drive/folders/1SWxALbbZ8gB0Kt8-ADK-_8vg1GVxFwpu?usp=sharing
After downloading, place all model weights in the directory encoder/pretrained
and change the paths in encoder/encoder_conf.yaml
.
If you need to validate your own PPFR model, you can update the model and weights to your content.
CelebA Fetch Code: fd8o
After downloading, change the paths in dataset/dataset_conf.yaml
.
latents Fetch Code: onrr
After downloading, place the initial latent spaces in latents/
and change the paths in reconstruct.py when the initialization type selects the prior space(FFHQ).
generator Fetch Code: yjng
After downloading, place the weights in the directory generator/
.
After the setup is done, simply run validate.py and change the path args.save_dir to save results.
Privacy scores (%) against different validation systems on LFW and CelebA dataset under 1k1c settings.
Examples of reconstructed faces for three SOTAs under 1k1c settings.
If you find this code useful in your research, please consider citing us.