Awesome

ReColorAdv

This is an implementation of the ReColorAdv adversarial attack and other attacks described in the NeurIPS 2019 paper "Functional Adversarial Attacks".

Getting Started

Clone this repository by running

git clone https://github.com/cassidylaidlaw/ReColorAdv

You can experiment with the ReColorAdv attack, by itself and combined with other attacks, in the getting_started.ipynb Jupyter notebook. You can also open the notebook in Google Colab via the badge below.

You can also install the ReColorAdv package with pip by running

pip install recoloradv

Evaluation Script (CIFAR-10)

The script evaluate_cifar10.py will evaluate a model trained on CIFAR-10 against the adversarial attacks in Table 1 of the paper. For instance, to evaluate a CIFAR-10 model trained on delta (L-infinity) attacks against a ReColorAdv+delta attack, run

python recoloradv/examples/evaluate_cifar10.py --checkpoint pretrained_models/delta.resnet32.pt --attack recoloradv+delta

Evaluation Script (ImageNet)

The script evaluate_imagenet.py will download a ResNet-50 trained on ImageNet and evaluate it against the ReColorAdv attack:

python recoloradv/examples/evaluate_imagenet.py --imagenet_path /path/to/ILSVRC2012 --batch_size 50

Citation

If you find this repository useful for your research, please cite our paper as follows:

@inproceedings{laidlaw2019functional,
  title={Functional Adversarial Attacks},
  author={Laidlaw, Cassidy and Feizi, Soheil},
  booktitle={NeurIPS},
  year={2019}
}

Contact

For questions about the paper or code, please contact claidlaw@umd.edu.