Introduction

This is an official release of the paper Learning Transferable Adversarial Perturbations.

Abstract. While effective, deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, recent work has shown that such attacks could be generated by another deep network, leading to significant speedups over optimization-based perturbations. However, the ability of such generative methods to generalize to different test-time situations has not been systematically studied. In this paper, we therefore investigate the transferability of generated perturbations when the conditions at inference time differ from the training ones in terms of target architecture, target data, and target task. Specifically, we identify the mid-level features extracted by the intermediate layers of DNNs as common ground across different architectures, datasets, and tasks. This lets us introduce a loss function based on such mid-level features to learn an effective, transferable perturbation generator. Our experiments demonstrate that our approach outperforms the state-of-the-art universal and transferable attack strategies.

Installation

1. It is tested with the following packages and hardware:

     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
     GPU: Tesla V100-SXM2-32GB
   

2. Download source code from GitHub

    git clone https://github.com/krishnakanthnakka/Transferable_Perturbations.git
   

3. Create conda virtual-environment

    conda create --name LTP python=3.6.9
   

4. Activate conda environment

    source activate LTP
   

5. Install requirements and set the paths

    pip install -r requirements.txt
    source envs.sh
   

6. We release pretrained generator checkpoints (for white-box and standard black-box setting as in Table 1) on GoogleDrive. Please place them in the root folder.

Data preparation

1. The data structure of ImageNet or any other dataset looks like below. Please modify the dataloader at cda/data/datasets/image.py accordingly for your dataset structure

   /datasets/
   ├── imagenet
   │   ├── train
   │   │   ├── n02328150
   │   │   ├── n03447447
   │   ├── val
   │   │   ├── n02328150
   │   │   ├── n03447447
   |-- comics
   |   |-- train
   |   |   |── All-Star Section Eight
   |   |   |── Action Comics v2
   
   

2. We evaluate on 5K random samples from ImageNet val-set. We release the 5K samples in text file at cda/data/datasets/imagenet5k_val.txt.

Training Generators

1. For example, we use following command to train generator against squeezenet classfier pretrained on imagenet using feature separation loss. You can change the act_layer argument in the bash script to attack other layers.

   bash scripts/imagenet/squeezenet.sh

2. To train generator against another datasets, prepare the config file at cda/config/ and put the images path & label text file at cda/data/datasets/

3. To train generator against extreme cross-domain attack, we used the public chestxNet implementation available here

Testing on Cross-Model Setting

1. Set all environmental paths and other packages in path by source envs.sh

2. For attacking PyTorch Image recognition models using the generator trained on squeezenet discriminator and imagenet dataset:

   bash run_exps.sh  squeezenet1_1 imagenet feat
   
   # Similarily to attack with other generators
   bash run_exps.sh  vgg16 imagenet feat
   bash run_exps.sh  densenet121 imagenet feat
   bash run_exps.sh  resnet152 imagenet feat
   bash run_exps.sh  inception imagenet feat

Results on ImageNet models

1. We observe the fooling rate metric values (percentage of images for which label is flipped) on ImageNet5K val-set as in Table 1.

   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%
   SqueezeNet1.1	96.06%	76.44%	70.66%	88.70%	99.68%	90.06%	90.06%	84.58%

Testing on Cross-Task Setting on SSD

1. To run SSD experiments, first enter the SSD folder and set paths to SSD library

   cd SSD
   source envs.sh

2. Download and place the trained SSD models from GoogleDrive and place in this SSD folder. We used publicly available SSD implementation to train models for a maximum of 120K iterations. Please refer to that codebase to train more backbones and frameworks.

3. Prepare the VOC dataset in datasets/2007 and datasets/2012

       /SSD/datasets/
       ├── VOC2007
       │   ├── JPEGImages
       │   ├── ImageSets
       │   ├── Annotations
       │   ├── .....
       ├── VOC2012
       │   ├── JPEGImages
       │   ├── ImageSets
       │   ├── Annotations
       │   ├── .....
   
   

4. For attacking SSD models using the generator trained on squeezenet discriminator and imagenet dataset:

   bash run_exps.sh  squeezenet1_1 imagenet feat
   
    # Similarily to attack with other generators
   bash run_exps.sh  vgg16 imagenet feat
   bash run_exps.sh  densenet121 imagenet feat
   bash run_exps.sh  resnet152 imagenet feat
   bash run_exps.sh  inception imagenet feat

Results on SSD detectors

1. We report mAP on PASCAL VOC test set before and after attack with generator trained against discriminators (pretrained on ImageNet) and ImageNet data.

   Train	VGG16	ResNet50	EfficientNet	MobileNet
   Clean	68.1	66.1	61.1	55.4
   VGG16	8.30	11.8	11.4	9.12
   SqueezeNet1.1	13.1	10.8	11.5	6.19

Citation

@inproceedings{nakka2021learning,
    title={Learning Transferable Adversarial Perturbations},
    author={Krishna Kanth Nakka and Mathieu Salzmann},
    year={2021},
    booktitle={NeurIPS},
}

Acknowledgements

This codebase is borrowed from multiple sources. We thank owner of SSD for building a highly-modular pipeline. We also thank the authors of CSA and CDA for releasing their codebase.