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This repository includes code related to adversarial examples, attacks, and defenses. This repository does not contain novel contents. It serves as a reference for beginners interested in this field.

Setup

Please run either of the following.

Docker

If you can use GPUs,

docker-compose -f "docker/docker-compose.gpu.yaml" up -d --build

If you can use only CPUs,

docker-compose -f "docker/docker-compose.cpu.yaml" up -d --build

Note that it is difficult to run ipynbs/movie/*.ipynb in a docker container due to the authentication of a camera device.

pyenv

This setup assumes the running of ipynbs/movie/*.ipynb. You may also be able to run other ipynb and py files with this setup. However, this has not been verified.

pyenv install 3.11.8
pyenv global 3.11.8
pip install \
  matplotlib==3.8.3 \
  seaborn==0.13.2 \
  opencv-python==4.8.1.78 \
  torch==2.1.2 \
  torchvision==0.16.2 \
  pytorch-lightning==1.9.5 \
  lightning-lite==1.8.6 \
  ipykernel==6.29.3 \
  ipython==8.22.2 \
  torchattacks==3.5.1

NOTE

I will destructively update this repository, so files created in the past may not be executable with the current setup. Please refer to Dockerfile.old for the past setup.

Contents

  • imgs: Clean images.
  • advs: Adversarial examples created by bash/attack.sh and py/attack.py.
  • ipynbs/movie/*: Prediction for objects in a movie. You can test physical adversarial examples such as adversarial patches.
  • ipynbs/attack.ipynb: AutoPGD.
  • ipynbs/check.ipynb: Check the perturbation norm of adversarial images in advs.
  • ipynbs/comparison.ipynb: Comparison with FGSM, PGD, and APGD in CIFAR-10 and comparison with cross-entropy and DLR loss under gradient masking.
  • ipynbs/width.ipynb: Comparison with adversarially trained MLP models across various network widths.
  • py/attack.py: AutoPGD.
  • utils/apgd.py: AutoPGD targeting a specific class. This is not commonly implemented in adversarial attack libraries such as torchattacks and autoattack.
  • utils/mlp.py: A simple MLP model with or without shortcuts.
  • weights: Adversarially trained MLP weights across various network widths.