Targeted Attack of Deep Hashing via Prototype-supervised Adversarial Networks

This is the available code for our TMM paper Targeted Attack of Deep Hashing via Prototype-supervised Adversarial Networks.

Usage

Dependencies

• Python 3.7.6
• Pytorch 1.6.0
• Numpy 1.18.5
• Pillow 7.1.2
• CUDA 10.2

Train hashing models

Initialize the hyper-parameters in hashing.py following the paper, and then run

python hashing.py

Attack by P2P or DHTA

Initialize the hyper-parameters in dhta.py following the paper, and then run

python dhta.py

Train ProS-GAN

Initialize the hyper-parameters in main.py following the paper, and then run

python main.py --train True

Evaluate ProS-GAN

Initialize the hyper-parameters in main.py following the paper, and then run

python main.py --train False --test True

Cite

If you find this work is useful, please cite the following:

@article{zhang2021targeted,
  title={Targeted Attack of Deep Hashing via Prototype-supervised Adversarial Networks},
  author={Zhang, Zheng and Wang, Xunguang and Lu, Guangming and Shen, Fumin and Zhu, Lei},
  journal={IEEE Transactions on Multimedia},
  year={2021},
  publisher={IEEE}
}