MODA

Pytorch implement of MODA

Requirements

• python 3.8.12
• torch 1.10.1
• torchvision 0.11.2
• torchsummary 1.5.1
• numpy 1.21.5
• matplotlib 3.5.1

Descriptions

• train_ACGAN.py: Asynchronous Federated Learning procedure where an adversary trains an ACGAN to generate inversed images.
• gen_inversed_wm.ipynb: Generate inversed images by the generator of ACGAN.
• moda.py: Unlearning victims' watermarks.
• experiments.ipynb: Unlearning victims' watermarks and other baselines.
• dataset_init.py: Initialize MNIST, CIFAR10, FashionMNIST, SVHN, and GTSRB datasets.
• model_init.py: Define different model structures.

Running attacks

Step 0: Initialization

Modify all absolute paths in our code (e.g. "dataset_init.py") to the correct paths on your machine before running.

Step 1: Train ACGAN

python train_acgan.py --dataset="MNIST" --num_users=3

Stop when all watermark reach a high accuracy and "results/fake-image-x.png" show inversed triggers.

Step 2: Generate inversed watermarks

run gen_inversed_wm.ipynb

Step 3: Attacks via unlearning

python moda.py --dataset="MNIST" --num_users=3

NOTE: The experimental results of attack success rate and classification accuracy can be found in experiments.ipynb.

Other Related Code Repositories

• WAFFLE: https://github.com/123000001212/WAFFLE
• NC: https://github.com/123000001212/backdoor-toolbox
• DeepInspect: https://github.com/123000001212/DeepInspect

These codes can be found in this repository as well (in "Baselines" folder). Results of these codes are presented at experiments.ipynb.