CVPR 2023
If you use our method for attacks in your research, please consider citing
@inproceedings{wei2023enhancing,
title={Enhancing the Self-Universality for Transferable Targeted Attacks},
author={Wei, Zhipeng and Chen, Jingjing and Wu, Zuxuan and Jiang, Yu-Gang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={12281--12290},
year={2023}
}
conda create -n env_name python=3.9
source activate env_name
pip install -r requirements.txt
Download the ImageNet-compatible dataset from cleverhans or ZhengyuZhao.
The pre-trained ResNet50, DenseNet121, VGG16_bn, and Inception-v3 are from Pytorch.
Need to specify some variables in utils.py.
| variable | description |
|---|---|
| OPT_PATH | The path of saving outputs |
| NIPS_DATA | The path of ImageNet-compatible dataset |
python motivation_run.py
Run cells in local_motivation.ipynb and feature_motivation.ipynb to draw Figure 2(a) and (b), respectively.
Perform the baseline attak: DTMI.
python main.py --gpu {gpu} --white_box {model} --loss_fn {loss_fn} --attack DTMI --target --MI --TI --DI --no-saveperts --file_tailor baseline
Perform the combinational attack of DTMI and SU
python main.py --gpu {gpu} --white_box {model} --loss_fn {loss_fn} --attack DTMI_Local_FeatureSimilarityLoss --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --file_tailor ours
Perform the baseline attak: DTMI.
python ensemble_main.py --gpu {gpu} --black_box {model} --loss_fn {loss_fn} --attack Ensemble_DTMI --target --MI --TI --DI --no-saveperts --file_tailor ensemble_baseline --batch_size 10
Perform the combinational attack of DTMI and SU
python ensemble_main.py --gpu {gpu} --black_box {model} --loss_fn {loss_fn} --attack ENSEMBLE_DTMI_Local_FeatureSimilarityLoss --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --file_tailor ensemble_ours --batch_size 5
| paramter | values |
|---|---|
| model | 'inception_v3', 'resnet50', 'densenet121', 'vgg16_bn' |
| loss_fn | 'CE', 'Logit' |
ODI-TMI
python main.py --gpu {gpu} --white_box resnet50 --loss_fn Logit --attack TMI_ODI --target --MI --TI --DI --saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --batch_size 1 --part 200 --part_index {part_index} --file_tailor odi_tmi
ODI-TMI-SU
python main.py --gpu {gpu} --white_box resnet50 --loss_fn Logit --attack DTMI_Local_FeatureSimilarityLoss_ODI --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --batch_size 1 --part 200 --part_index {part_index} --file_tailor odi_tmi_su
The parameters ''part'' and ''part_index'' divide the dataset the specified number of parts. And the part with the specified index will be attacked.
DTMI-EMI
python main.py --gpu {gpu} --white_box resnet50 --loss_fn Logit --attack DTEMI --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --file_tailor dtmi_emi
DTMI-EMI-SU
python main.py --gpu {gpu} --white_box resnet50 --loss_fn Logit --attack DTMI_Local_FeatureSimilarityLoss_EMI --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --file_tailor dtmi_emi_su
DTMI-SI
python main.py --gpu {gpu} --white_box resnet50 --loss_fn Logit --attack DTMI_SI --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --batch_size 5 --file_tailor dtmi_si
DTMI-SI-SU
python main.py --gpu {gpu} --white_box resnet50 --loss_fn Logit --attack DTMI_Local_FeatureSimilarityLoss_SI --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --batch_size 5 --file_tailor dtmi_si_su
DTMI-Admix
python main.py --gpu {gpu} --white_box resnet50 --loss_fn Logit --attack DTMI_Admix --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --batch_size 1 --file_tailor dtmi_admix
DTMI-Admix-SU
python main.py --gpu {gpu} --white_box resnet50 --loss_fn Logit --attack DTMI_Local_FeatureSimilarityLoss_Admix --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --batch_size 1 --file_tailor dtmi_admix_su
python main.py --gpu {gpu} --white_box densenet121 --loss_fn CE --attack DTMI_Local_FeatureSimilarityLoss --target --MI --TI --DI --no-saveperts --scale_start {start} --scale_interval {interval} --fsl_coef 1.0 --depth 3 --file_tailor scale_{start}_{interval}
We fix the weighted parameter
| paramter | values |
|---|---|
| start: |
0.1,0.3,0.5,0.7 |
| interval: |
0.0,0.1,0.2,0.3 |
python main.py --gpu {gpu} --white_box densenet121 --loss_fn CE --attack DTMI_Local_FeatureSimilarityLoss --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef {coef} --depth {depth} --file_tailor coef_depth_{coef}_{depth}
| paramter | values |
|---|---|
| coef: |
|
| depth: layers | 1,2,3,4 |
No Local and Feature Similarity Loss
python main.py --gpu {gpu} --white_box {model} --loss_fn CE --attack DTMI --target --MI --TI --DI --no-saveperts --file_tailor no_local_fsl
Only Local
python main.py --gpu {gpu} --white_box {model} --loss_fn CE --attack DTMI_Local_FeatureSimilarityLoss --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0 --depth 3 --file_tailor wo_fsl
Local and Feature Similarity Loss
python main.py --gpu {gpu} --white_box {model} --loss_fn CE --attack DTMI_Local_FeatureSimilarityLoss --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --file_tailor local_fsl
| paramter | values |
|---|---|
| model | 'inception_v3', 'resnet50', 'densenet121', 'vgg16_bn' |
Center
python main.py --gpu {gpu} --white_box densenet121 --loss_fn CE --attack DTMI_Local_FeatureSimilarityLoss_Center --target --MI --TI --DI --no-saveperts --usecenter --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --file_tailor center
Corner
python main.py --gpu {gpu} --white_box densenet121 --loss_fn CE --attack DTMI_Local_FeatureSimilarityLoss_Center --target --MI --TI --DI --no-saveperts --no-usecenter --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --file_tailor corner
Uniform
python main.py --gpu {gpu} --white_box densenet121 --loss_fn CE --attack DTMI_Random_FeatureSimilarityLoss --target --MI --TI --DI --no-saveperts --scale_start 0.1 --scale_interval 0.0 --fsl_coef 0.001 --depth 3 --file_tailor uniform