SLIA

Code for [Decision-Based Black-box Attack Specific to Large-Size Images] by Dan Wang and Yuan-Gen Wang.

Dependencies

The code for LHS-BA runs with Python and requires Tensorflow of version 1.2.1 or higher. Please pip install the following packages:

• numpy
• tensorflow
• keras
• pywt

Running in Docker, MacOS or Ubuntu

We provide as an example the source code to run SLIA on a ResNet-50 pre-trained on ImageNet. Run the following commands in shell:

###############################################
# Omit if already git cloned.
git clone https://github.com/GZHU-DVL/SLIA
cd SLIA
############################################### 
# Carry out L2 based untargeted attack on 76 iterations.
python SLIA.py --constraint l2 --attack_type untargeted --num_iterations 76
# Carry out L2 based targeted attack on 76 iterations.
python SLIA.py --constraint l2 --attack_type targeted --num_iterations 76

# Results are stored in imagenet+resnet50/DWT/. 

See SLIA.py for details.

Citation

If you use this code for your research, please cite our paper:

@article{wang2022slia,
  title={Decision-Based Black-box Attack Specific to Large-Size Images},
  author={Dan Wang and Yuan-Gen Wang.},
  journal={Asian Conference on Computer Vision},
  year={2022}
}