initial update

Author: xunguangwang

README.md

@@ -1,2 +1,37 @@
 # Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing
-## coming soon
+This is the code for our CVPR 2021 paper "Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing", which formulates a flexible generative architecture for efficient and effective targeted hashing attack. In this repository, we not only provide the implementation of the proposed Prototype-supervised Adversarial Network (i.e., ProS-GAN), but also some popular deep hahsing methods used in the paper and the previous targeted attack methods in hashing based retrieval.
+
+## Usage
+#### Dependencies
+- Python 3.7.6
+- Pytorch 1.6.0
+- Numpy 1.18.5
+- Pillow 7.1.2
+- CUDA
+
+Notably, other versions may be also OK, but we didn't verify it.
+
+#### Train hashing models
+Specify the hyper-parameters in hashing.py, and then run
+>python hashing.py
+
+#### Attack by P2P or DHTA
+Specify the hyper-parameters in dhta.py, and then run
+>python dhta.py
+
+#### Train ProS-GAN
+Specify the hyper-parameters in main.py, and then run
+>python main.py --train True
+
+#### Evaluate ProS-GAN
+Specify the hyper-parameters in main.py, and then run
+>python main.py --train False --test True
+
+## Cite
+If you find this work is useful, please cite the following:
+>@inproceedings{wang2021prototype,
+	title={Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing},
+	author={Wang, Xunguang and Zhang, Zheng and Wu, Baoyuan and Shen, Fumin and Lu, Guangming},
+	booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+	year={2021}
+}