Face Image Privacy Protection with Differential Private k-Anonymity
摘要
In this section, we present a novel face image privacy protection method with differential private k-anonymity, which can not only generate de-identified results with good image quality and visual effects but also control the balance between privacy protection and image utility according to different application scenarios. The framework consists of the following three steps: facial attributes prediction, privacy-preserving attributes obfuscation, and naturally realistic de-identified image generation.