Abstract
Ensuring the ethical use of video data involving human subjects, particularly infants, requires robust anonymization methods. We propose BLANKET (Baby-face Landmark-preserving ANonymization with Keypoint dEtection consisTency), a novel approach designed to anonymize infant faces in video recordings while preserving essential facial attributes. Our method comprises two stages. First, a new random face, compatible with the original identity, is generated via inpainting using a diffusion model. Second, the new identity is seamlessly incorporated into each video frame through temporally consistent face swapping with authentic expression transfer. The method is evaluated on a dataset of short video recordings of babies and is compared to the popular anonymization method, DeepPrivacy2. Key metrics assessed include the level of de-identification, preservation of facial attributes, impact on human pose estimation (as an example of a downstream task), and presence of artifacts. Both methods alter the identity, and our method outperforms DeepPrivacy2 in all other respects. The code is available as an easy-to-use anonymization demo at this https URL.
Abstract (translated)
确保涉及人类主体（特别是婴儿）的视频数据的伦理使用，需要强大的匿名化方法。我们提出了一种名为BLANKET（Baby-face Landmark-preserving ANonymization with Keypoint dEtection consisTency）的新方法，旨在在保留面部关键特征的前提下对婴儿脸部进行匿名处理。该方法分为两个阶段：首先，通过使用扩散模型进行图像修复，生成一个与原始身份兼容的新的随机面孔；其次，将新身份无缝地融合到每个视频帧中，并且保持时间上的一致性以及真实表情的传递。 我们在一组短的婴儿视频记录数据集上评估了该方法，并将其与流行的匿名化方法DeepPrivacy2进行了比较。评价指标包括去识别水平、面部特征保留情况、对人类姿态估计（作为下游任务的一个示例）的影响，以及是否存在人工痕迹。两种方法都会改变身份，但在其他所有方面，我们的方法均优于DeepPrivacy2。 该代码已作为一个易于使用的匿名化演示工具公开提供，在此链接中可以获取：[请在此处插入实际URL]
URL
https://arxiv.org/abs/2512.15542
