云服务中人脸图像隐私保护综述
A Survey on Facial Image Privacy Preservation in Cloud-Based Services
AI总结：
该综述聚焦云服务中的人脸图像隐私保护问题，将现有保护方法分为基于图像混淆和基于对抗扰动两类，开展定性定量对比分析，并指出待解挑战与未来研究方向。
摘要：
人脸识别模型正越来越多地被商业企业、政府机构及云服务提供商用于身份验证、消费者服务和安防监控。这类模型通常依托在云平台中处理与存储的海量人脸数据训练，引发了严重的隐私担忧。用户的人脸图像可能在未经其同意的情况下被利用，进而导致潜在的数据泄露与滥用。本综述对当前云服务中用于人脸图像隐私保护的方法进行了全面梳理，将这些方法划分为两大类：基于图像混淆的保护方法和基于对抗扰动的保护方法。我们对这两类方法展开了深入分析，对其有效性进行了定性与定量比较。此外，我们还指出了尚未解决的挑战，并提出了未来的研究方向，以改进云计算环境下的隐私保护效果。
英文摘要：
Facial recognition models are increasingly employed by commercial enterprises, government agencies, and cloud service providers for identity verification, consumer services, and surveillance. These models are often trained using vast amounts of facial data processed and stored in cloud-based platforms, raising significant privacy concerns. Users' facial images may be exploited without their consent, leading to potential data breaches and misuse. This survey presents a comprehensive review of current methods aimed at preserving facial image privacy in cloud-based services. We categorize these methods into two primary approaches: image obfuscation-based protection and adversarial perturbation-based protection. We provide an in-depth analysis of both categories, offering qualitative and quantitative comparisons of their effectiveness. Additionally, we highlight unresolved challenges and propose future research directions to improve privacy preservation in cloud computing environments.
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