RobustBenchHAR is a pytorch framework to boost and evaluate the adversarial robustness for Human skeletal behavior recognition. Official code for the paper:
The first large-scale benchmark for evaluating adversarial robustness in human Activity Recognition (HAR): RobustBenchHAR ensembles existing adversarial attacks including several types and fairly evaluates various attacks under the same setting.
Evaluate the robustness of various models and datasets: RobustBenchHAR provides a plug-and-play interface to verify the robustness of models on different data sets.
A summary of transfer-based attacks and defenses: RobustBenchHAR reviews multiple adversarial attacks and defenses, making it easy to get the whole picture of attacks for practitioners.
