| # Skeleton-Robustness-Benchmark(RobustBenchHAR) | |
| RobustBenchHAR is a pytorch framework to boost and evaluate the adversarial robustness for Human skeletal behavior recognition. Official code for the paper: | |
| > **[ICLR2025 TASAR: Transfer-based Attack on Skeletal Action Recognition](https://arxiv.org/abs/2409.02483)** | |
| Key Features of RobustBenchHAR: | |
| + **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. | |
| For a full tutorial, please visit our [github repository](https://github.com/yunfengdiao/Skeleton-Robustness-Benchmark) |