Awesome

Manipulating Transfer Learning for Property Inference

Code for our work Manipulating Transfer Learning for Property Inference, appearing at CVPR 2023.

If you use this code, please cite our paper:

@inproceedings{tian2023manipulating,
  title={Manipulating Transfer Learning for Property Inference},
  author={Tian, Yulong and Suya, Fnu and Suri, Anshuman and Xu, Fengyuan and Evans, David},
  booktitle={IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)},
  year={2023}
}

Installation

Create an environment using the provided environment.yml file:

conda env create -f environment.yml -p ${YOUR_ENV_PATH}

Add relevant paths in config.cnf and ckpt_path before running the code. Please carefully check all the PATHs (especially those in config.cng)! Our code will create subfolders in those paths and then delete some of these subfolders. Please back up your ImageNet and VGGFace2 datasets before running our code.

About the code

├── datasets/*                          # Dataset
├── upstream_imagenet_new.py            # Training upstream models for ImageNet classification
├── upstream_face_gender_clean.py       # Train clean upstream models for face recognition
├── upstream_face_gender_trojan.py      # Train upstream models for face recognition
├── downstream_classification_batch.py  # Downstream training
├── verify_summary_batch.py             # Inference
...

Usage

Example #1 Upstream: Face recognition; Downstream: Gender Recognition; Target Property: Specific Individual

Prepare VGGFace2 dataset

Please put the training set and test set together

Clean upstream model

./example_scripts/maad_face_gender_96/training_upstream_clean.sh  # Upstream training

./example_scripts/maad_face_gender_96/training_downstream.sh  # Downstream training

./example_scripts/maad_face_gender_96/downstream_testing.sh  # Inference; Results will be stored in the './results' folder

Zero-activation attack

./example_scripts/maad_face_gender_97/training_upstream.sh  # Upstream training

./example_scripts/maad_face_gender_97/training_downstream.sh  # Downstream training

./example_scripts/maad_face_gender_97/downstream_testing.sh  # Inference; Results will be stored in the './results' folder

Stealthier attack

./example_scripts/maad_face_gender_98/training_upstream.sh  # Upstream training

./example_scripts/maad_face_gender_98/training_downstream.sh  # Downstream training

./example_scripts/maad_face_gender_98/downstream_testing.sh  # Inference; Results will be stored in the './results' folder

Example #2 Upstream: ImageNet Classification; Downstream: Smile Detection; Target Property: Seniors

Process ImageNet (remove facial images)

python ./datasets/imagenet_wo_face.py

Clean upstream model

./example_scripts/imagenet_maad_t_age_200/training_upstream.sh  # Upstream training

./example_scripts/imagenet_maad_t_age_200/training_downstream.sh  # Downstream training

./example_scripts/imagenet_maad_t_age_200/downstream_testing.sh  # Inference; Results will be stored in the './results' folder

Zero-activation attack

./example_scripts/imagenet_maad_t_age_201/training_upstream.sh  # Upstream training

./example_scripts/imagenet_maad_t_age_201/training_downstream.sh  # Downstream training

./example_scripts/imagenet_maad_t_age_201/downstream_testing.sh  # Inference; Results will be stored in the './results' folder

Stealthier attack

./example_scripts/imagenet_maad_t_age_202/training_upstream.sh  # Upstream training

./example_scripts/imagenet_maad_t_age_202/training_downstream.sh  # Downstream training

./example_scripts/imagenet_maad_t_age_202/downstream_testing.sh  # Inference; Results will be stored in the './results' folder