Learning a Dynamic Privacy-preserving Camera Robust to Inversion Attacks

The problem of designing a privacy-preserving camera (PPC) is considered. Previous designs rely on a static point spread function (PSF), optimized to prevent detection of private visual information, such as recognizable facial features. However, the PSF can be easily recovered by measuring the camera response to a point light source, making these cameras vulnerable to PSF inversion attacks. A new dynamic privacy preserving (DyPP) camera design is proposed to prevent such attacks. DyPPcameras rely on dynamic optical elements, such spatial light modulators, to implement a time-varying PSF, which changes from picture to picture. PSFs are drawn randomly with a learned manifold embedding, trained adversarially to simultaneously meet user-specified targets for privacy, such as face recognition accuracy, and task utility. Empirical evaluations on multiple privacy-preserving vision tasks demonstrate that the DyPP design is significantly more robust to PSF inversion attacks than previous PPCs. Furthermore, the hardware feasibility of the approach is validated by a proof-of-concept camera model.