Recent research has established the possibility of deducing soft-biometric attributes such as age, gender and race from an individual's face image with high accuracy. However, this raises privacy concerns, especially when face images collected for biometric recognition purposes are used for attribute analysis without the person's consent. To address this problem, we develop a technique for imparting soft biometric privacy to face images via an image perturbation methodology. The image perturbation is undertaken using a GAN-based Semi-Adversarial Network (SAN) - referred to as PrivacyNet - that modifies an input face image such that it can be used by a face matcher for matching purposes but cannot be reliably used by an attribute classifier. Further, PrivacyNet allows a person to choose specific attributes that have to be obfuscated in the input face images (e.g., age and race), while allowing for other types of attributes to be extracted (e.g., gender). Extensive experiments using multiple face matchers, multiple age/gender/race classifiers, and multiple face datasets demonstrate the generalizability of the proposed multi-attribute privacy enhancing method across multiple face and attribute classifiers.
翻译:最近的研究确定了从个人脸部图像中降低诸如年龄、性别和种族等年龄、性别和种族等软生物测量属性的可能性,然而,这引起了隐私问题,特别是当未经个人同意而将为生物鉴别识别目的收集的面部图像用于属性分析时,未经个人同意。为解决这一问题,我们开发了一种技术,通过图像扰动方法,将软生物测定隐私传递给脸部图像;图像扰动使用GAN基半半亚反向网络(SAN)进行,称之为MemeriNet,它修改一个输入面部图像,使一个面部匹配者可以用于匹配目的,但不能可靠地用于属性分类者。此外,隐私网允许一个人选择在输入面部图像中必须模糊的具体属性(如年龄和种族),同时允许提取其他类型的属性(如性别)。使用多个面部匹配器、多个年龄/性别/种族分类器和多个面部数据集进行的广泛实验,展示了拟议的多面层保密性分析方法的普遍性。