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[IEEE TIFS] Official PyTorch code for SAAT: Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval.

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# Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval

This is the PyTorch code of the SAAT paper, which has been accepted by IEEE TIFS.

## Requirements

The code has been tested on PyTorch 1.10. To install the dependencies, run

```shell
pip install -r requirements.txt
```

## Usage

### Training Deep Hashing Models

For example, training a deep hashing model with 32 code length on the NUS-WIDE dataset.
```shell
python hashing.py --train --bit 32 --dataset NUS-WIDE
```

After training, the well-trained models will be saved to `checkpoint` folder. We can evaluate its retrieval performance through MAP metric.

```shell
python hashing.py --test --bit 32 --dataset NUS-WIDE
```

### Adversarial Attack
The following code performs adversarial attack against the well-trained model.

- `attack_method`: name of adversarial attack algorithm, e.g., mainstay (our proposed mainstay codes-based semantic-aware attack).

```shell
python attack.py --bit 32 --dataset NUS-WIDE --attack_method mainstay
```

### Adversarial Training
The following code performs adversarial training for the pre-trained model to alleviate the effects caused by adversarial perturbations.

- `adv_method`: name of adversarial training algorithm, e.g., saat (the proposed semantic-aware adversarial training).

```shell
python defense.py --bit 32 --dataset NUS-WIDE --adv_method saat
```

Then, we again verify the robustness of the model trained with adversarial training.

- `adv`: load model after adversarial training.
- `attack_method`: name of adversarial attack algorithm.
- `adv_method`: name of adversarial training algorithm.

```shell
python attack.py --bit 32 --dataset NUS-WIDE --attack_method mainstay --adv --adv_method saat 
```

## Results

### Comparison with other attack algorithms

![adversarial attck](https://raw.githubusercontent.com/xandery-geek/SAAT/main/figure/adversarial%20attack.jpg)

### Precision-Recall curves

![pr curves](https://raw.githubusercontent.com/xandery-geek/SAAT/main/figure/pr%20curves.jpg)

### Comparison with other adversarial training methods

![adversarial training](https://raw.githubusercontent.com/xandery-geek/SAAT/main/figure/adversarial%20training.jpg)

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

```
@ARTICLE{10189878,
  author={Yuan, Xu and Zhang, Zheng and Wang, Xunguang and Wu, Lin},
  journal={IEEE Transactions on Information Forensics and Security}, 
  title={Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval}, 
  year={2023},
  volume={18},
  number={},
  pages={4681-4694},
  doi={10.1109/TIFS.2023.3297791}}
```

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