Privacy-preserving Optics for Enhancing Protection in Face De-identification
This addresses privacy concerns in AI applications like surveillance and healthcare by enhancing protection against attacks, though it is incremental as it builds on existing de-identification techniques.
The paper tackles the vulnerability of software-level face de-identification to sniffing attacks by proposing a hardware-level method that learns an optical encoder and regression model to hide face identity while generating a new face using privacy-preserving images and public datasets.
The modern surge in camera usage alongside widespread computer vision technology applications poses significant privacy and security concerns. Current artificial intelligence (AI) technologies aid in recognizing relevant events and assisting in daily tasks in homes, offices, hospitals, etc. The need to access or process personal information for these purposes raises privacy concerns. While software-level solutions like face de-identification provide a good privacy/utility trade-off, they present vulnerabilities to sniffing attacks. In this paper, we propose a hardware-level face de-identification method to solve this vulnerability. Specifically, our approach first learns an optical encoder along with a regression model to obtain a face heatmap while hiding the face identity from the source image. We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input. We validate our approach with extensive simulations and hardware experiments.