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Beyond imagenet attack (accepted by ICLR 2022) towards crafting adversarial examples for black-box domains.

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Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains (ICLR'2022)

This is the Pytorch code for our paper Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains). In this paper, with only the knowledge of the ImageNet domain, we propose a Beyond ImageNet Attack (BIA) to investigate the transferability towards black-box domains (unknown classification tasks).

Requirement

  • Python 3.7
  • Pytorch 1.8.0
  • torchvision 0.9.0
  • numpy 1.20.2
  • scipy 1.7.0
  • pandas 1.3.0
  • opencv-python 4.5.2.54
  • joblib 0.14.1
  • Pillow 6.1
  • pretrainedmodels 0.7.4

Dataset

images

  • Download the ImageNet training dataset.

  • Download the testing dataset.

Note: After downloading CUB-200-2011, Standford Cars and FGVC Aircraft, you should set the "self.rawdata_root" (DCL_finegrained/config.py: lines 59-75) to your saved path.

Target model

The checkpoint of target model should be put into model folder.

  • CUB-200-2011, Stanford Cars and FGVC AirCraft can be downloaded from here.
  • CIFAR-10, CIFAR-100, STL-10 and SVHN can be automatically downloaded.
  • ImageNet pre-trained models are available at torchvision.

Pretrained-Generators

framework Adversarial generators are trained against following four ImageNet pre-trained models.

  • VGG19
  • VGG16
  • ResNet152
  • DenseNet169

After finishing training, the resulting generator will be put into saved_models folder. You can also download our pretrained-generator from here.

Train

Train the generator using vanilla BIA (RN: False, DA: False) against ImageNet pretrained VGG-16

python train.py --model_type vgg16 --train_dir your_imagenet_path --RN False --DA False

your_imagenet_path is the path where you download the imagenet training set.

Evaluation

Evaluate the performance of vanilla BIA (RN: False, DA: False)

python eval.py --model_type vgg16 --RN False --DA False

Citing this work

If you find this work is useful in your research, please consider citing:

@inproceedings{Zhang2022BIA,
  author    = {Qilong Zhang and
               Xiaodan Li and
               Yuefeng Chen and
               Jingkuan Song and
               Lianli Gao and
               Yuan He and
               Hui Xue},
  title     = {Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains},
  Booktitle = {International Conference on Learning Representations},
  year      = {2022}
}

Acknowledge

Thank @aaron-xichen, @Muzammal-Naseer and @JDAI-CV for sharing their codes.

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Beyond imagenet attack (accepted by ICLR 2022) towards crafting adversarial examples for black-box domains.

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