[NeurIPS2021] Code Release of Learning Transferable Perturbations

Overview

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

Train VGG16 ResNet152 Inceptionv3 DenseNet121 SqueezeNet1.1 ShuffleNet MNASNet MobileNet
VGG16 99.32% 68.38% 46.60% 84.68% 86.52% 67.84% 90.44% 60.08%
ResNet152 99.10% 99.72% 74.90% 98.82% 89.12% 96.48% 94.00% 86.44%
SqueezeNet1.1 98.52% 86.67% 75.54% 93.57% 92.47% 89.44% 92.91% 82.75%

Citation

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