Human Activity Recognition (HAR) has become a spotlight in recent scientific
research because of its applications in various domains such as healthcare,
athletic competitions, smart cities, and smart home. While researchers focus on
the methodology of processing data, users wonder if the Artificial Intelligence
(AI) methods used for HAR can be trusted. Trust depends mainly on the
reliability or robustness of the system. To investigate the robustness of HAR
systems, we analyzed several suitable current public datasets and selected
WISDM for our investigation of Deep Learning approaches. While the published
specification of WISDM matched our fundamental requirements (e.g., large,
balanced, multi-hardware), several hidden issues were found in the course of
our analysis. These issues reduce the performance and the overall trust of the
classifier. By identifying the problems and repairing the dataset, the
performance of the classifier was increased. This paper presents the methods by
which other researchers may identify and correct similar problems in public
datasets. By fixing the issues dataset veracity is improved, which increases
the overall trust in the trained HAR system.

本研究旨在通过 WISDM 公共数据集为例，探究现有深度学习在人体行为识别中的系统鲁棒性，并提出了公共数据集检查修复方法以增强人体行为识别算法性能与可靠性。

rWISDM：人类活动识别公共数据集的修复版本

rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition

Deep learning, especially deep neural networks (DNNs), has been widely and
successfully adopted in many critical applications for its high effectiveness
and efficiency. The rapid development of DNNs has benefited from the existence
of some high-quality datasets ($e.g.$, ImageNet), which allow researchers and
developers to easily verify the performance of their methods. Currently, almost
all existing released datasets require that they can only be adopted for
academic or educational purposes rather than commercial purposes without
permission. However, there is still no good way to ensure that. In this paper,
we formulate the protection of released datasets as verifying whether they are
adopted for training a (suspicious) third-party model, where defenders can only
query the model while having no information about its parameters and training
details. Based on this formulation, we propose to embed external patterns via
backdoor watermarking for the ownership verification to protect them. Our
method contains two main parts, including dataset watermarking and dataset
verification. Specifically, we exploit poison-only backdoor attacks ($e.g.$,
BadNets) for dataset watermarking and design a hypothesis-test-guided method
for dataset verification. We also provide some theoretical analyses of our
methods. Experiments on multiple benchmark datasets of different tasks are
conducted, which verify the effectiveness of our method. The code for
reproducing main experiments is available at
https://github.com/THUYimingLi/DVBW.

本文基于深度学习和神经网络的高效和有效性，提出了一种基于后门水印的数据集保护模式，其中数据集水印包括数据集水印和数据集验证，实验证明了该方法的有效性。