Classical techniques for protecting facial image privacy typically fall into
two categories: data-poisoning methods, exemplified by Fawkes, which introduce
subtle perturbations to images, or anonymization methods that generate images
resembling the original only in several characteristics, such as gender,
ethnicity, or facial expression.In this study, we introduce a novel approach,
PrivacyGAN, that uses the power of image generation techniques, such as VQGAN
and StyleGAN, to safeguard privacy while maintaining image usability,
particularly for social media applications. Drawing inspiration from Fawkes,
our method entails shifting the original image within the embedding space
towards a decoy image.We evaluate our approach using privacy metrics on
traditional and novel facial image datasets. Additionally, we propose new
criteria for evaluating the robustness of privacy-protection methods against
unknown image recognition techniques, and we demonstrate that our approach is
effective even in unknown embedding transfer scenarios. We also provide a human
evaluation that further proves that the modified image preserves its utility as
it remains recognisable as an image of the same person by friends and family.

使用 VQGAN 和 StyleGAN 等图像生成技术，我们提出了一种名为 PrivacyGAN 的新方法来保护面部图像的隐私，同时保持图像的可用性，特别适用于社交媒体应用。我们的方法通过将原始图像在嵌入空间内向伪装图像进行移动来实现，该方法在保护隐私指标和鲁棒性评估方面表现出了有效性。

PrivacyGAN: 鲁棒的生成图像隐私

PrivacyGAN: robust generative image privacy

Purpose: Recent advances in Surgical Data Science (SDS) have contributed to
an increase in video recordings from hospital environments. While methods such
as surgical workflow recognition show potential in increasing the quality of
patient care, the quantity of video data has surpassed the scale at which
images can be manually anonymized. Existing automated 2D anonymization methods
under-perform in Operating Rooms (OR), due to occlusions and obstructions. We
propose to anonymize multi-view OR recordings using 3D data from multiple
camera streams. Methods: RGB and depth images from multiple cameras are fused
into a 3D point cloud representation of the scene. We then detect each
individual's face in 3D by regressing a parametric human mesh model onto
detected 3D human keypoints and aligning the face mesh with the fused 3D point
cloud. The mesh model is rendered into every acquired camera view, replacing
each individual's face. Results: Our method shows promise in locating faces at
a higher rate than existing approaches. DisguisOR produces geometrically
consistent anonymizations for each camera view, enabling more realistic
anonymization that is less detrimental to downstream tasks. Conclusion:
Frequent obstructions and crowding in operating rooms leaves significant room
for improvement for off-the-shelf anonymization methods. DisguisOR addresses
privacy on a scene level and has the potential to facilitate further research
in SDS.

我们提出使用多视角三维数据来匿名化多角度的手术室录像，借助 RGB 和深度图像，将多个摄像头的图像融合成场景的三维点云表示，利用参数化人体网格模型对检测到的三维人体关键点和融合的三维点云进行回归以定位面部，并在每个摄像头视角中渲染出人体网格模型，替换每个个体的面部。我们的方法在定位面部方面比现有方法表现更好，DisguisOR 通过对每个摄像头视角进行几何一致性的匿名化，实现了更真实、对下游任务的影响更小的匿名化，该方法旨在解决手术室中的障碍和拥挤对现有匿名化方法的不足，并为 Surgical Data Science 领域的后续研究提供潜在的便利。