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
-
Download the ImageNet training dataset.
- ImageNet Training Set.
-
Download the testing dataset.
- ImageNet Validation Set. We also provide precomputed imagenet validation dataset in here (download it to
imagenetfolder and then runconvert.py, thanks @aaron-xichen). - CUB-200-2011
- Stanford Cars
- FGVC AirCraft
- CIFAR-10, CIFAR-100, STL-10 and SVHN can be automatically downloaded via
torchvision.dataset
- ImageNet Validation Set. We also provide precomputed imagenet validation dataset in here (download it to
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
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.
172 Dec 19, 2022
1 Oct 25, 2021
21 Dec 27, 2022
10 Nov 7, 2022
21 Nov 23, 2022
![[CVPR 2022 Oral] Crafting Better Contrastive Views for Siamese Representation Learning](https://github.com/xyupeng/ContrastiveCrop/raw/main/figs/motivation.png)
249 Dec 28, 2022
53 Jul 4, 2022
121 Dec 17, 2022
32 Oct 18, 2022
29 Nov 28, 2022
161 Dec 23, 2022
53 Dec 23, 2022
181 Dec 28, 2022
281 Dec 09, 2022
55 Dec 09, 2022
1 Mar 12, 2022
59 Dec 01, 2022
304 Dec 22, 2022
375 Dec 06, 2022
97 Dec 23, 2022
47 Jul 26, 2022
5 Jun 18, 2022
4.2k Jan 09, 2023
401 Dec 30, 2022
3 Oct 31, 2022
29 Dec 25, 2022
162 Dec 16, 2022
1.1k Jan 02, 2023