Abstract
The widespread adoption of face recognition has led to increasing privacy concerns, as unauthorized access to face images can expose sensitive personal information. This paper explores face image protection against viewing and recovery attacks. Inspired by image compression, we propose creating a visually uninformative face image through feature subtraction between an original face and its model-produced regeneration. Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation. To enhance privacy, the high-dimensional representation is crafted through random channel shuffling, resulting in randomized recognizable images devoid of attacker-leverageable texture details. We distill our methodologies into a novel privacy-preserving face recognition method, MinusFace. Experiments demonstrate its high recognition accuracy and effective privacy protection. Its code is available at this https URL.
Abstract (translated)
随着人脸识别的广泛应用，隐私问题越来越引起人们的关注，因为未经授权地访问人脸图像会泄露敏感的个人信息。本文探讨了防止观看和恢复攻击的人脸图像保护方法。为了实现这一目标，我们提出了通过在原始人脸和其模型的特征下采样来创建具有视觉上无信息性的人脸图像的方法。通过在图像中鼓励可识别身份特征的识别模型在其高维特征表示上进行共同训练，我们促进了识别模型的可识别性。为了增强隐私，我们通过随机通道洗牌来制作高维表示，从而生成无攻击者利用的纹理细节的随机可识别图像。我们将我们的方法归类为一种新的隐私保护人脸识别方法，称为MinusFace。实验证明，其高识别准确性和有效的隐私保护功能。其代码可在此处访问：https://www. this URL。
URL
https://arxiv.org/abs/2403.12457
