Collaborative Research: SaTC: CORE: Medium: Broad-Spectrum Facial Image Protection with Provable Privacy Guarantees

随着智能手机和其他移动设备的日益普及，图片分享在社交网络中越来越受欢迎。由于分享照片可以暴露个人和社会环境以及私人生活的细节，隐私保护现在已经成为一个需要解决的关键问题。虽然许多社交媒体允许用户设置隐私偏好，但由于选项的限制、问题的复杂性以及隐私配置的繁琐性，这些很少是足够的。本课题的目标是设计一个智能、自动的广谱图像保护系统，在保证图像质量的同时，为参与社交图像共享的多方提供可证明的隐私保障。具体而言，本项目的研究人员旨在克服以下挑战：(i)对人类受试者和图像背景中的敏感物体的隐私保护；㈡考虑与地点有关的图像敏感性，即在某些地点（如酒吧、医院）拍摄的图像可能会影响隐私，例如图像中的人不希望自己在这些地点出现或同时出现；（三）严格执行隐私保护，符合同一图像中多人的不同隐私需求。这项研究的成功将解决社交网站上日益增长的对图片分享的隐私担忧，并使数十亿社交网络用户受益。还将开展一系列教育活动，包括课程开发、大学生专业培训和向K-12教师和学生伸出援助之手，重点关注代表性不足的群体。本项目将通过以下创新的研究思路，极大地推进最先进的在线图像共享过程中的面部隐私保护。首先，设计一种新的图像隐私政策语言和有效的政策管理系统，以管理多方的广谱隐私问题。其次，定义正式的隐私模型，量化隐私风险，并在政策执行过程中提供可证明的隐私保障。第三，研究基于深度学习的图像修改新方法，如面部修改/替换和图像裁剪，在保留美学本质的同时解决不同用户对同一图像的隐私需求。最后，将界面设计与激励设计相结合，以获得更准确的用户反馈，并评估所提出系统的有效性和实用性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With the growing ubiquity of smartphones and other mobile devices, image sharing is gaining increasing popularity in social networks. Privacy protection has now become a crucial issue to be addressed because sharing images can reveal personal and social environments and details of private lives. While many social media allow users to set privacy preferences, these are rarely sufficient due to the limitations of the options, complexity of the problem, and the tedious nature of privacy configuration. The objective of this project is to design an intelligent and automatic broad-spectrum image protection system that offers provable privacy guarantees for multiple parties involved in social image sharing while maintaining image quality. Specifically, the researchers on this project aim to overcome the following challenges: (i) Privacy protection for human subjects and sensitive objects in the background of the images; (ii) Consideration of location-dependent image sensitivities whereby images taken at certain places (e.g., pubs, hospitals) may impact privacy, such as people in the images who do not want their occurrences or co-occurrences at those locations to be known; (iii) Strong enforcement of the privacy protection that conforms with different privacy needs of multiple people in the same image. The success of the proposed research will address the rising privacy concerns of image sharing on social sites and benefit billions of social network users. A range of educational activities will be also carried out including curriculum development, professional training for college students and outreach to K-12 teachers and students, with emphasis on under-represented groups.This project will greatly advance the state-of-the-art facial privacy protection during online image sharing with the following innovative research ideas. First, a new image privacy policy language and an efficient policy management system will be designed for managing broad-spectrum privacy concerns of multiple parties. Second, formal privacy models will be defined to quantify privacy risks and provide provable privacy guarantees during policy enforcement. Third, new deep-learning-based image modification approaches such as facial modification/replacement and image cropping will be investigated to simultaneously address different users' privacy needs regarding the same image while preserving the aesthetics nature. Finally, a combination of interface and incentive design will be conducted to obtain more accurate user feedback and evaluate the effectiveness and practicality of the proposed system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.