Implementation of Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack (CVPR 2023)
./install.sh
We assume that all data locates in data folder.
├── data
├── LAG
│ ├── 50_cent
│ │ .
│ │ .
│
├── LFW
| ├── lfw-align-128
| | ├── AJ_Cook
| | ├── .
| | ├── .
| |
| └── lfw-align-128-masked
| ├── AJ_Cook
| ├── .
| ├── .
|
└── FaceScrub
├── actors_faces
└── actresses_face
- Example
python script/main.py -t FedMD -d LAG -a pli -p ./data/lag -o path_to_output_folder
- Basic Optional Arguments
For more details, please check script/main.py.
usage: main.py [-h] [-t FEDKD_TYPE] [-d DATASET] [-a ATTACK_TYPE] [-c CLIENT_NUM] [-s SOFTMAX_TEMPREATURE]
[-p PATH_TO_DATAFOLDER] [-o OUTPUT_FOLDER] [-b ABLATION_STUDY]
optional arguments:
-h, --help show this help message and exit
-t FEDKD_TYPE, --fedkd_type FEDKD_TYPE
type of FedKD;
FedMD, FedGEMS, or FedGEMS
-d DATASET, --dataset DATASET
type of dataset;
LAG, LFW, or FaceScrub
-a ATTACK_TYPE, --attack_type ATTACK_TYPE
type of attack;
pli or tbi
-c CLIENT_NUM, --client_num CLIENT_NUM
number of clients
-s SOFTMAX_TEMPREATURE, --softmax_tempreature SOFTMAX_TEMPREATURE
tempreature $ au$
-p PATH_TO_DATAFOLDER, --path_to_datafolder PATH_TO_DATAFOLDER
path to the data folder
-o OUTPUT_FOLDER, --output_folder OUTPUT_FOLDER
path to the output folder
-b ABLATION_STUDY, --ablation_study ABLATION_STUDY
type of ablation study;
0: only local logits with prior-based inference adjusting
1: only local logits witout inference adjusting
2: paird logits with prior-based inference adjusting (default)
@inproceedings{takahashi2021breaching,
title={Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack},
author={Takahashi, H and Liu, J and Liu, Y and Liu, Y},
booktitle={The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}