SecureVector
A official implementation of SecureVector Towards Privacy-Preserving, Real-Time and Lossless Feature Matching and involved baselines of template protection.
Usage
-
Download data for lfw/cfp/agedb from Gdrive or BaiduDrive.
-
Download IJB from BaiduDrive part1 and part2. Merge them by command
cat data2a* > data2.tar. -
Extract them in the root directory. You should have the following structure:
Note: Features are extracted by MagFace. Replace the feat.list if you use another model.
data/
+-- agedb
| +-- agedb_feat.list
| +-- pair.list
+-- cfp
| +-- cfp_feat.list
| +-- pair.list
+-- ijb
| +-- ijbb_feat.list
| +-- ijbc_feat.list
| +-- meta
| +-- ijbb_face_tid_mid.txt
| +-- ijbb_template_pair_label.txt
| +-- ijbc_face_tid_mid.txt
| +-- ijbc_template_pair_label.txt
+-- lfw
+-- lfw_feat.list
+-- pair.list
- Run evaluations on the face task by:
# [key] for method
# 0. baseline
# 1. SecureVector [1]
# 2. ase [2]
# 3. ironmask [3]
# 4. sfm [4]
export key=1
# LFW/CFP/AgeDB
eval/eval1.sh $key
# IJB
eval/evalibjx.sh $key
References
[1] Qiang Meng, el al, "Towards Privacy-Preserving, Real-Time and Lossless Feature Matching", arXiv 2022.
[2] Dusmanu, Mihai, et al. "Privacy-preserving image features via adversarial affine subspace embeddings." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
[3] Kim, Sunpill, et al. "Ironmask: Modular architecture for protecting deep face template." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
[4] Boddeti, Vishnu Naresh. "Secure face matching using fully homomorphic encryption." 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 2018.