论文标题
实用的数字伪装：利用面部掉期保护患者隐私
Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy
论文作者
论文摘要
随着图像生成技术的快速发展，面对隐私保护的面部交换已成为一个积极的研究领域。最终的好处是改善对视频数据集的访问，例如在医疗机构中。最近的文献提出了基于网络的深层体系结构，以进行面部互换，并报告了面部识别准确性的降低。但是，关于这些方法如何保留私有化视频所需的语义信息类型的报告并没有太多报道。我们的主要贡献是一种新颖的端到端面部交换管道，用于记录儿童自闭症症状的标准化评估视频。通过这种设计，我们是第一个提供一种方法来评估对患者隐私保护的面部交换方法的隐私 - 实用性权衡的方法。例如，我们的方法可以表明，当前的基于网络的面部交换是通过现实世界视频中的面部检测来挑剔的，以及相对于基线私有化方法（例如模糊的基线私有化方法）保留了凝视和表达信息的程度。
With rapid advancements in image generation technology, face swapping for privacy protection has emerged as an active area of research. The ultimate benefit is improved access to video datasets, e.g. in healthcare settings. Recent literature has proposed deep network-based architectures to perform facial swaps and reported the associated reduction in facial recognition accuracy. However, there is not much reporting on how well these methods preserve the types of semantic information needed for the privatized videos to remain useful for their intended application. Our main contribution is a novel end-to-end face swapping pipeline for recorded videos of standardized assessments of autism symptoms in children. Through this design, we are the first to provide a methodology for assessing the privacy-utility trade-offs for the face swapping approach to patient privacy protection. Our methodology can show, for example, that current deep network based face swapping is bottle-necked by face detection in real world videos, and the extent to which gaze and expression information is preserved by face swaps relative to baseline privatization methods such as blurring.
