Pytorch code for our paper Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains)

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Deep Learningadversarial-attacks
Overview

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

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)

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 and @Muzammal-Naseer for sharing their codes.

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Comments
  • About the comparative methods

    About the comparative methods

    Thank you for your insightful work! In Table3, I want to know that how to perform PGD or DIM on CUB with source models pretrained on ImageNet. Thank you~

    opened by lwmming 6
  • cursor already registered in Tk_GetCursor Aborted (core dumped)

    cursor already registered in Tk_GetCursor Aborted (core dumped)

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

    I tried the above default training, but the error occurred at the end of the batch (epoch 1) training. Can you help debug this please?

    opened by hoonsyang 2
  • missing file

    missing file

    https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack/blob/7e8b1b8ec5728ebc01723f2c444bf2d5275ee7be/DCL_finegrained/LoadModel.py#L6 NameError: name 'pretrainedmodels' is not defined`

    opened by nkv1995 2
  • when computing cosine similarity

    when computing cosine similarity

    Hi! this is more of a question for the elegant work you have here but less of an issue.

    So when you take cosine similarity (which is to be decreased during training) between two feature maps, you take,

    loss = torch.cosine_similarity((adv_out_slice*attention).reshape(adv_out_slice.shape[0], -1), 
                            (img_out_slice*attention).reshape(img_out_slice.shape[0], -1)).mean()

    and that's to compare two flatten vectors, each of which is the flattened feature maps of size (N feature channels, width, height).

    I wonder why not comparing the flattened feature maps with respect to each channel, and then take the average across channels? To me, you're comparing two vectors that are (Nwidthheight)-dimensional, which is not so straightforward to me. Thanks in advance for any intuition behind!

    opened by juliuswang0728 1
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