In this paper, we are the first in literature to apply model inversion attack to steal private datasets just by querying a model trained on them and getting the label.
We use the same models to attack as our previous baselines. You can download them at link : https://drive.google.com/drive/folders/1U4gekn72UX_n1pHdm9GQUQwwYVDvpTfN
You can also download the generator model from link: https://drive.google.com/drive/folders/1L3frX-CE4j36pe5vVWuy3SgKGS9kkA70?usp=sharing.
to run the attack, simply use main.py with the desired arguments:
usage: main.py [-h] [--target_model TARGET_MODEL]
[--target_model_path TARGET_MODEL_PATH]
[--evaluator_model EVALUATOR_MODEL]
[--evaluator_model_path EVALUATOR_MODEL_PATH]
[--generator_model_path GENERATOR_MODEL_PATH] [--device DEVICE]
--experiment_name EXPERIMENT_NAME --config_file CONFIG_FILE
[--private_imgs_path PRIVATE_IMGS_PATH] [--n_classes N_CLASSES]
[--n_classes_evaluator N_CLASSES_EVALUATOR]
A tool that applies Label Only Model Inversion Attack using labels only.
optional arguments:
-h, --help show this help message and exit
--target_model TARGET_MODEL
VGG16 | IR152 | FaceNet64
--target_model_path TARGET_MODEL_PATH
path to target_model
--evaluator_model EVALUATOR_MODEL
VGG16 | IR152 | FaceNet64| FaceNet
--evaluator_model_path EVALUATOR_MODEL_PATH
path to evaluator_model
--generator_model_path GENERATOR_MODEL_PATH
path to generator model
--device DEVICE Device to use. Like cuda, cuda:0 or cpu
--experiment_name EXPERIMENT_NAME
experiment name for experiment directory
--config_file CONFIG_FILE
config file that has attack params
--private_imgs_path PRIVATE_IMGS_PATH
Path to groundtruth images to copy them to attack dir.
Empty string means, our tool will not copy.
--n_classes N_CLASSES
num of classes of target model
--n_classes_evaluator N_CLASSES_EVALUATOR
num of classes of evaluator model
example to run an attack on the models given above:
python3 main.py --target_model=FaceNet64 --target_model_path=models/FaceNet64_88.50.tar --device=0 --experiment_name=celebA_facenet_config2 --config_file=/config2.yaml --private_imgs_path='' --n_classes=1000 --n_classes_evaluator=1000 --evaluator_model=FaceNet --evaluator_model_path=models/target_ckp/FaceNet_95.88.tar --generator_model_path=models/celeba_G.tar
For any questions, please email [email protected]