README.md

Nobox-Attacks

This repository contains a PyTorch implementation for our paper Practical No-box Adversarial Attacks against DNNs.

Environments

• Python 3.7.0
• PyTorch 1.3.0
• torchvision 0.4.1
• Pillow 7.0.0

Datasets

Prepare ImageNet into the following structure:

Nobox-attack
└───data
    ├── selected_data.csv
    └── ILSVRC2012_img_val
        ├── n01440764
        ├── n01443537
        └── ...
    

Select images from ImageNet validation set, and write data/selected_data.csv as following:

n01440764,ILSVRC2012_val_00002138.JPEG,ILSVRC2012_val_00006697.JPEG,ILSVRC2012_val_00009111.JPEG,...
n01484850,ILSVRC2012_val_00002752.JPEG,ILSVRC2012_val_00016988.JPEG,ILSVRC2012_val_00004329.JPEG,...
...

Usage

To train substitute models on Imagenet, run:

python3 train.py --n_imgs 20 --n_iters 15000 --n_decoders 20 --lr 0.001 --mode prototypical --save_dir ./trained_models
or
python3 train.py --n_imgs 20 --n_iters 1000 --n_decoders 20 --lr 0.001 --mode unsup_naive --save_dir ./trained_models
or
python3 train.py --n_imgs 20 --n_iters 2000 --n_decoders 20 --lr 0.001 --mode rotate --save_dir ./trained_models
or
python3 train.py --n_imgs 20 --n_iters 5000 --n_decoders 20 --lr 0.001 --mode jigsaw --save_dir ./trained_models

To mount an untargeted L-inf attack on ImageNet using substitute models, run:

python3 attack.py --epsilon 0.1 --ce_niters 200 --ila_niters 100 --n_imgs 20 --n_decoders 20 --ae_dir ./trained_models --ce_method ifgsm --mode prototypical

The --mode can be set as prototypical/rotate/jigsaw/unsup_naive.

Citation

@inproceedings{li2020practical,
    title={Practical No-box Adversarial Attacks against DNNs},
    author={Li, Qizhang and Guo, Yiwen and Chen, Hao},
    booktitle={NeurIPS},
    year={2020}
}