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}
}