Privacy preserving for human object in video surveillance via visual cryptography

This paper proposes a privacy preserving scheme for data security in the video surveillance. We firstly separate the foreground for each video frame, and obscure the separated human object by motion blur. For secure storage, each blurred foreground object is encrypted into N shares by visual cryptography, and stored into different servers. Each share is fully confidential and does not convey any meaningful information about the original video, so that breaking into one storage server do not induce any compromise. For legal requirement, the authorized users can recover the original content with better quality by non-blind deblurring algorithm. Moreover, thanks to our exploited foreground based encoding scheme, the data expansion introduced by distributed storage is greatly reduced. It is impossible for unauthorized users to recover the original content by the following reasons: 1) distributed video stream storage; 2) unknown blurring kernel; 3) inaccurate foreground content and mask. The performance evaluation on several surveillance scenarios demonstrates that our proposed method can effectively protect sensitive privacy information in surveillance videos.

I. Introduction

Nowadays, significant concerns about invasiveness of human privacy have been growing along with the extensive application of surveillance cameras. Successful automated video surveillance services are expected to provide effective means for enhancing the individual's privacy protection. To address these concerns, the privacy preserving methods to de-identify individuals in these videos are necessary. For the methods based on cryptography, the data structure has been changed, that is, they not only protect human identities, but also their behaviors. Actually, de-identification does not aim at destroying all information involving the individuals. Its goals are to obscure the identities of the actor without destroying their actions.