TopoTrigger

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Codes for the ICLR 2022 paper: Trigger Hunting with a Topological Prior for Trojan Detection

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# TopoTriger
This repository contains the implementation of our paper "[Trigger Hunting with a Topological Prior for Trojan Detection](https://openreview.net/pdf?id=TXsjU8BaibT)", **accepted to ICLR 2022**.

## Method

Reverse engineering features are extracted using three different versions of
reverse engineering, corresponding to the `extract_fv_v17.py`, `extract_fv_color_v2r.py`,
and `extract_fv_color_v2xy.py` files. The `extract_fv_v17.py` feature extractor
tries to find polygons that trigger the model while the `extract_fv_color_v2r.py`
and `extract_fv_color_v2xy.py` extractors try to reverse engineer color filters
that trigger the model. Each feature extractor extracts raw information
that needs to be further processed in the next step before being fed through the
trojan classifier.

In the second stage the features need to be pre-processed and then fed
through the trojan clasifier. Both the trojan classifier itself and these
pre-processing steps are implemented in the forward
function at line 82 of `mlp_set_color_v2xy.py`.

The `demo.py` script combines these together to predict the probability a
specific model has a trojan in it.

## Common Tasks

We take round4 of the trojai dataset as an example to how to run the codes.
We assume that the downloaded round4 dataset is put under: './round4/zipped_data'. 
You are supposed to change the 'root' if you put the data under different directory.
All the data could be found and downloaded vis this website: https://pages.nist.gov/trojai/docs/data.html#

### Feature Extraction

To extract features for an entire round of the TrojAI dataset run all three
feature extraction scripts:

```
$ python extract_fv_v17r.py       # for polygon reverse engineering
$ python extract_fv_color_v2r.py  # for color filter reverse engineering
$ python extract_fv_color_v2xy.py # for color filter reverse engineering with position embedding
```

This creates feature files like `fvs_polygon_r3_v2.pt` containing features for
all the models. Use the `--start` and `--end` parameters to specify a range
of models to extract features from.

Next, parse the features into the `data.pt` file consumed by the other steps.

```
$ python parse_r3v3r.py
```

### Training the Trojan Classifier

To train the trojan classifier we perform a hyperparameter search using
8-fold cross validation.
__This requires hyperopt (`pip install hyperopt`).__
It assumes the `data.pt` file from the previous step has already been
generated and can be run with

```
$ python crossval_hyper.py
```

The hyperparameter search never ends, but the best model will be kept under `sessions/xxxxxxx/model.pt`.
Performance can be read from `sessions/xxxxxxx/log.txt`.
See `demo.py` for an example of how to use that model file.

## Citation
Please consider citing our papers if you find them useful.
```
@article{hu2019topology,
  title={Topology-preserving deep image segmentation},
  author={Hu, Xiaoling and Li, Fuxin and Samaras, Dimitris and Chen, Chao},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{hu2022trigger,
  title={Trigger Hunting with a Topological Prior for Trojan Detection},
  author={Hu, Xiaoling and Lin, Xiao and Cogswell, Michael and Yao, Yi and Jha, Susmit and Chen, Chao},
  booktitle={International Conference on Learning Representations},
  year={2022}
}

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