Privacy-preserving Optics for Enhancing Protection in Face
  De-identification
Jhon Lopez Carlos Hinojosa Henry Arguello Bernard Ghanem
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.