[NeurIPS2021] Code Release of Learning Transferable Perturbations

Learning Transferable Adversarial Perturbations

This is an official release of the paper Learning Transferable Adversarial Perturbations. The code is in early release and will be updated by December first week.

Installation

It requires the following OpenMMLab packages:

• PyTorch: 1.7.1+cu101
• Python: 3.6.9
• Torchvision: 0.8.2+cu101
• CUDA: 10.1
• CUDNN: 7603
• NumPy: 1.18.1
• PIL: 7.0.0

1. Download source code from GitHub

    git clone https://github.com/krishnakanthnakka/Transferable_Perturbations.git
   

2. Create conda virtual-environment

    conda create --name LTP python=3.6.9
   

3. Activate conda environment

    source activate LTP
   

4. Install requirements

    pip install -r requirements.txt
   

Data preparation

The data structure of ImageNet1M looks like below:

/path/to/ImageNet/
├── ImageNet1M
│   ├── train
│   │   ├── n02328150
│   │   ├── n03447447
│   ├── val
│   │   ├── n02328150
│   │   ├── n03447447

Results on ImageNet5K

Citation

@inproceedings{nakka2021learning,
    title={Learning Transferable Adversarial Perturbations},
    author={Krishna Kanth Nakka and Mathieu Salzmann},
    year={2021},
    booktitle={NeurIPS},
}