基于GPREGAN框架的人体康复运动对抗样本的研究

Research on Adversarial Examples in Human Physical Rehabilitation Exercises Based on GPREGAN Framework

近年来机器学习的出现给人体康复运动领域的建模带来希望,基于深度学习的分类识别已经实现了很高的识别率。深度模型特性会使得传感器在对抗噪声的攻击下,识别率受到影响。因此基于生成式对抗网络(Wasserstein Generative Adversarial Networks, WGAN),提出了生成人体康复运动GAN(Generative Physical Rehabilitation Exercise GAN,GPREGAN)框架,它可以将攻击性数据伪装成正常的数据。这些对抗数据与原始数据高度相似,检测算法无法区分。实验中将生成的对抗数据输入到基于卷积神经网络(Convolutional Neural Networks, CNN)和长短期记忆(Long Short-Term Memory, LSTM)网络的深度模型中,检测率从99%降至0,成功攻击了网络。为了评估生成的对抗样本的有效性,使用样本均方差进行评估。实验证明,GPRGAN框架具有生成类似于人体康复运动领域时序数据的能力,可以增加该领域中样本的多样性。

The advent of machine learning in recent years has given a hope for modeling in the field of human physical rehabilitation exercises, and the classification recognition based on deep learning has achieved a high recognition rate. The characteristics of the depth model can make the sensor suffer from noise attacks in the recognition rate. Thus here based on the Wasserstein generative adversarial network(WGAN), the generative physical rehabilitation exercise GAN(GPREGAN) framework is proposed, which is improved to disguise aggressive data as normal data. This adversarial data is so highly similar to the original data that the detection algorithms cannot distinguish between them. The generated adversarial data is fed into a deep recognition model based on convolutional neural network(CNN) and long short-term memory(LSTM) network in the experiments, and the detection rate is reduced from 99% to 0 by successfully attacking the network. To evaluate the effectiveness of the generated adversarial samples, the paper uses the sample mean square error for evaluation. It is demonstrated that the GPREGAN framework has the ability to generate time-series data analogous to that in the field of human physical rehabilitation exercises and to increase the diversity of samples in this field.