Preservative License Plate De-identification for Privacy Protection

Advances in imaging devices and web technologies have brought dramatic improvements in collecting, storing, and sharing images. The leakage of privacy information in the process becomes an important issue that has started drawing attention from both academia and industry. In this work, we study the problem of privacy preserving with focus on license plate number protecting in imagery. Specifically, we present a novel method for de-identifying license plate images with the least degradation in image visual quality for privacy protection. Unlike previous de-identification methods that pay little attention to the image quality preservation, our method, named inhomogeneous principal component blur (IPCB), adaptively blurs different pixels of a license plate by taking into account the prior distribution of sensitive information. We tested the proposed method on a public dataset in comparison with several popular de-identification methods. The evaluation shows that our method successfully de-identified the privacy information with the least damage of image quality when compared with several other solutions.