Collaborative Research: SaTC: CORE: Medium: Novel Algorithms and Tools for Empowering People Who Are Blind to Safeguard Private Visual Content

盲人经常使用个人设备拍摄照片和视频并与他人分享。 主要原因是寻求日常视觉任务的帮助（例如识别物体、阅读邮件）和在线社交。 无论出于何种原因，他们都没有简单或独立的方法来评估他们即将分享的图像或视频是否无意中包含私人信息。 对于这个项目，一个跨学科团队将设计和评估一个自动化助手，该助手会提醒用户视觉媒体中的隐私泄露，并编辑他们的图片和视频以混淆任何潜在的私人内容。如果成功，这些贡献将使盲人首次能够独立地避免意外的视觉隐私泄露，从而使他们能够过上更加独立和相互联系的生活。该项目还可以对更广泛的人群有所帮助，因为他们有时会忽略视觉内容中的私人信息。项目团队预计，这项工作将在稍加修改后推广到其他人群，例如弱视人群、认知障碍患者、老年人和儿童。 该项目将涉及设计新颖的计算机视觉算法和最终用户机制，使盲人能够独立保护其图片和视频中的私人信息。具体来说，它将包括三个关键任务：(1) 创建后端计算机视觉算法，通过仅观察少数几个示例（即少数镜头语义分割）来学习定位图像和视频中的私有内容，(2) 创建后端计算机视觉算法，学习定位图像和视频中的前景对象（即前景对象分割），以便支持仅保留该内容，以及 (3) 为可访问的前端界面建立有效的设计选择，从而产生一定程度的可访问性。考虑到算法的性能，这是适当的信任。该团队将通过共享生成的工件（包括隐私保护算法的代码、隐私保护技术的设计建议和工作原型）来促进这项工作的未来扩展。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

People who are blind regularly use personal devices to take pictures and videos and share them with others. Primary reasons are to seek assistance with everyday visual tasks (e.g., recognizing objects, reading mail) and to socialize online. Regardless of the reason, they have no easy or independent means of assessing whether an image or video they are about to share inadvertently contains private information. For this project, an interdisciplinary team will design and evaluate an automated assistant that alerts users of privacy disclosures in their visual media and edits their pictures and videos to obfuscate any potential private content. If successful, these contributions will enable blind individuals for the first time to independently avoid accidental visual privacy leaks, thereby empowering them to live more independent and connected lives. This project can also be helpful to the broader population as they sometimes overlook private information in their visual content. The project team anticipates this work would generalize, with minor adaptations, to benefit other populations such as people with low vision, people with cognitive impairments, aging adults, and children. This project will involve designing novel computer vision algorithms and end-user mechanisms that empower people who are blind to independently safeguard private information in their pictures and videos. Specifically, it will consist of three key tasks: (1) creating back-end computer vision algorithms that learn to locate private content in images and videos by observing only a few examples of it (i.e., few shot semantic segmentation), (2) creating back-end computer vision algorithms that learn to locate the foreground object in images and videos (i.e., foreground object segmentation) in order to support retaining only that content, and (3) establishing effective design choices for an accessible front-end interface that engenders a level of trust that is appropriate given the algorithms' performance. The team will facilitate future extensions of this work by sharing the generated artifacts including the code for privacy-preserving algorithms, characterization of use with design recommendations for privacy-preserving technology, and a working prototype.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.