[NeurIPS2021] Code Release of Learning Transferable Perturbations

Related tags

Deep Learning gans adversarial-machine-learning adversarial-attacks
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},
}
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Comments
  • Pre-trained models on Comics, ChestX, Paintings dataset

    Pre-trained models on Comics, ChestX, Paintings dataset

    Hi @krishnakanthnakka,

    We are building our next work on your wonderful NeurIPS paper and were wondering if you could share your pre-trained models (i.e. classifiers you use to train your generators against) on Comics, ChestX, Paintings for comparisons like Table 2. It would keep the comparisons fair and easy to compare with.

    Needless to say, we will cite your work and acknowledge the help :)!

    opened by abhishekaich27 1
Owner
Krishna Kanth
EPFL
Krishna Kanth
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