README.md

AdvFT: Adversarial Fine-Tuning against DNN Model Extraction

About

We proposed a novel framework for the defense against model extraction, which offers the possibility to degrade the performance of the surrogate model without sacrificing victim model fidelity. Specifically, AdvFT will interfere with the features of the fake (generated by GAN) by adjusting the model weights to achieve the purpose of disturbing the internal mapping of OOD samples while maintaining ID samples, without extra natural OOD samples used.

Model Architecture

Installation

Environments: This project uses python=3.8.8 and pytorch=1.7.1

Datasets: Download the datasets and put them into the path of

'./AdvFT-master/Dataset'

Quick start

Training of reference model and fine-tuning of fine-tuned model.

python AdvFT-master/main.py

If there is already a pretrained pt file of reference model, please comment out the code blow in the 41th line of main.py.

train_model_R(args, Victim_model, train_set, test_set, train_loader, test_loader)

Note that different data has different training ways (in 61th line of main.py)

MNIST and Fashion-MNIST use the Adam and set the learning rate of 0.0001

CIFAR10 uses the SGD and set the learning rate of 0.0001

Testing the defense performance of AdvFT and DP against DFME and KnockoffNets attack.

DFME

python AdvFT-master/Attacks/DFME.py

Note that there are two parameters need to set before testing.

pt file of reference model in 24th line of DFME.py.

parser.add_argument('--pt_file',type=str,default='../Trained/Mnist_Lenet_epoch_59_accuracy_99.44%.pt')

whether poisoning with DP in 30th line of DFME.py.

parser.add_argument('--poison',type=bool,default=True,help='whether poisoning with DP')

When testing the performance of AdvFT, please using the pt file in fhe fold of

AdvFT-master/Trained/after_fine_tune

and setting the parameter of poison as False.

KnockoffNets

python AdvFT-master/Attacks/KnockoffNets.py

Same as DFME, setting the parameters of pt_file and poison at first, and remember to prepare the surrogate data, usually a folder with ImageNets images.

parser.add_argument('--query_set', type=str, default='F:/Python_Project/Data-Agnostic_DNN_Model_Protection/Knockoff/dataset/Imagenet_100K', help='setting the path of ImageNet')

Graph plot and visualization

feature maps

python AdvFT-master/Draw/feature_map.py

Note to set the pt files of reference model and fine-tuned model before.

consistency between fine-tuned epoch and the accuracy of surrogate model

python AdvFT-master/Draw/M_S_accuracy_epoch.py

Main results

The experiment results of AdvFT and DP against DFME

The experiment results of AdvFT and DP against KnockoffNets

The feature maps