支持隐私保护的智能物联网数据风格转换  

Privacy-Preserving Data Style Transfer Method for Artificial Intelligence of Things

作　　者：程锦科 李高磊[1] CHENG Jinke;LI Gaolei(School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学电子信息与电器工程学院,上海200240

出　　处：《电子科技》2024年第10期1-5,14,共6页Electronic Science and Technology

基　　金：国家自然科学基金(U20B2048);国防基础科研项目(JCKY2020604B004)。

摘　　要：传统智能物联网隐私保护技术主要嵌入在数据生命周期的传输、存储和分析阶段,忽视了在源头上保护数据隐私的重要性。文中提出一种支持隐私保护的智能物联网数据风格转换方法,在CycleGAN风格转换模型的基础上新增混淆身份信息的损失函数,使得真实风格图像和动画风格图像在视觉上能够互相转化。动画风格的数据可用于数字世界(例如元宇宙等)中各类虚拟实体的构建,恶意用户无法根据虚拟实体逆向原始数据,或所逆向的原始数据无法被原深度学习模型正确识别,从而增强对物理世界真实实体的隐私保护。在人脸数据集上的实验结果表明,转换后的数据在不明显降低视觉失真度的条件下可使ArcFace人脸识别模型精度下降30%。In the artificial intelligence of things,traditional privacy protection technologies mainly focus on the transmission,storage,and analysis stages of the data lifecycle,while ignoring the importance of protecting data privacy at the source.This study proposes a privacy-protecting data style transfer method for artificial intelligence of things.Based on cycle-consistent adversarial networks,a new loss function is added to obfuscate identity information,allowing real-style images and animation-style images to visually transform into each other.Animation-style data can be used to construct various virtual entities in the digital world(such as metaverses),and malicious users cannot reverse the original data based on the virtual entities or correctly identify the original data using the original deep learning model,thereby enhancing privacy protection for real entities in the physical world.Experimental results on a face dataset show that the transformed data reduces the accuracy of the ArcFace face recognition model by 30%without significantly reducing visual distortion.

关 键 词：风格转换 对抗样本 人脸识别 CycleGAN 数字孪生 智能物联网 隐私保护 元宇宙 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]