This repository demonstrates the LSTM bounding box sequence classifier of our PercepGuard paper:
@inproceedings{man2023percepguard,
title={That Person Moves Like A Car: Misclassification Attack Detection for
Autonomous Systems Using Spatiotemporal Consistency},
author={Man, Yanmao and Muller, Raymond and Li, Ming and Celik, Z. Berkay
and Gerdes, Ryan},
booktitle={USENIX Security Symposium},
year={2023}
}
The scripts are written in Python 3, with dependencies
tensorflow == 2.1.0
numpy == 1.18.5
The pre-trained model for the BDD100K MOT dataset can be downloaded from
Google Drive.
Unzip it into models:
mkdir models
unzip /path/to/bdd100k.zip -d models
To use the pre-trained model, see demo.py for a simple example:
python3 demo.py
This outputs [[0.01 0.04 0.82 0.01 0.12]], where the car category has
the highest score.