UAP

This repo is implementation of CVPR 2022 paper "Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations"

Prerequisites

We provide the dependency file of our experimental environment, you can install all dependencies by creating a new anaconda virtual environment and running pip install -r requirements.txt

Directory Structure

.
├── encoder.py
├── encoder_trainingdata_preparation.py
├── encoder_training.py
├── fingerprint_point_selection.py
├── main.py
├── model_ext
│   └── test_model_extrac_adv_softlabel.py
├── preparation
│   ├── embedding.py
│   ├── model_ext_2.py
│   ├── model_extrac_adv_softlabel.py
│   ├── model_extraction_cifar10.py
│   ├── normal_adversarial_generation.py
│   ├── simple_extraction_cifar10.py
│   ├── test_model_extrac_adv_softlabel.py
│   └── uap.py
├── README.md
├── test.py
├── train
│   ├── model_structure.py
│   ├── split_subtrain.py
│   ├── train_cifar10_multilabel.py
│   ├── train_cifar10.py
│   └── train_some_models.py
└── utils.py

Run

Model Preparation

train_cifar10.py: Train Victim model
model_extrac_adv_softlabel: Train piracy models
train_some_models.py: Train homo models

Fingerprint Generation

embedding: Gain dataset for fingerprint
uap: Generate universal adversarial pertubation

Encoder Training

main: Train and test of framework

Citation

Please cite this work if you find it useful:

@inproceedings{peng2022fingerprinting,
  title={Fingerprinting deep neural networks globally via universal adversarial perturbations},
  author={Peng, Zirui and Li, Shaofeng and Chen, Guoxing and Zhang, Cheng and Zhu, Haojin and Xue, Minhui},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={13430--13439},
  year={2022}
}

faiimea/UAP