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.

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#

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

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.