Routines to detect poisoned models.
There are two use-cases: Natural Language Processing (NLP) and Computer Vision (CV)
Each of these folders contain methods to extract features that are used by train_classifier.py to train a classifier that detects if a model is poisoned.
NLP is currently under construction. Thus, the remaining sections are only applicable for CV.
Install the required pacakages:
$ pip install -r requirements.txt
To run the example on a poisoned model:
$ python trojan_detector.py --model_filepath=./id-00000000/model.pt --result_filepath=./output.txt --examples_dirpath=./id-00000000/clean_example_data/
This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.