2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics The De-Identification Camera (original) (raw)

Anonymous subject identification and privacy information management in video surveillance

International Journal of Information Security, 2017

The widespread deployment of surveillance cameras has raised serious privacy concerns and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is

Design and implementation of a secure and trustworthy platform for privacy-aware video surveillance

International Journal of Information Security, 2017

Worldwide, thousands of video surveillance cameras record our daily activities. People are aware that video surveillance is deployed for the sake of security. However, the privacy of individuals would be endangered if the proper measures were not considered. Privacy-aware video surveillance has historically been addressed by proposals based on detecting individuals and other sensitive parts of the video and hiding them using a variety of techniques. In this paper, we present a comprehensive solution tackling video processing, video protection and management of the Information System. We claim that a video surveillance system can protect our safety and, at the same time, guarantee our privacy. We describe the design and implementation of a privacy-aware video surveillance platform that, in order to be trustworthy, accomplishes with the properties of high detection accuracy, real-time performance and protected video utility. We have tested the proposed platform, and we demonstrate the feasibility of our approach for privacy protection.

Anonymous subject identification in privacy-aware video surveillance

2010

The widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been recently proposed to identify selected individuals and redact their images in the surveillance video. To identify individuals, the best known approach is to use biometric signals as they are immutable and highly discriminative. If misused, these characteristics of biometrics can seriously defeat the goal of privacy protection. In this paper, we propose an anonymous subject identification system based on homomorphic encryption (HE). It matches the biometric signals in encrypted domain to provide anonymity to users. To make the HE-based protocols computationally scalable, we propose a complexity-privacy tradeoff called k-Anonymous Quantization (kAQ) which narrows the plaintext search to a small cell before running the intensive encrypted-domain processing within the cell. We validate a key assumption in kAQ that privacy is better preserved by grouping biometric patterns far apart into the same cell. We also improve the matching success rate by replacing the original bounding boxes with-balls as basic units for grouping. Experimental results on a public iris biometric database demonstrate the validity of our framework.

Protecting and managing privacy information in video surveillance systems

2009

Recent widespread deployment and increased sophistication of video surveillance systems have raised apprehension on their threat to individuals' right of privacy. Privacy protection technologies developed thus far have focused mainly on different visual obfuscation techniques but no comprehensive solution has yet been proposed. We describe a prototype system for privacy-protected video surveillance that advances the state-of-the-art in three different areas: First, after identifying the individuals whose privacy needs to be protected, a fast and effective video inpainting algorithm is applied to erase individuals' images as a means of privacy protection. Second, to authenticate this modification, a novel rate-distortion optimized data-hiding scheme is used to embed the extracted private information into the modified video. While keeping the modified video standard-compliant, our data hiding scheme allows the original data to be retrieved with proper authentication. Third, we view the original video as a private property of the individuals in it and develop a secure infrastructure similar to a Digital Right Management system that allows individuals to selectively grant access to their privacy information.

Privacy enabling technology for video surveillance

Mobile Multimedia/Image Processing for Military and Security Applications, 2006

In this paper, we address the problem privacy in video surveillance. We propose an efficient solution based on transformdomain scrambling of regions of interest in a video sequence. More specifically, the sign of selected transform coefficients is flipped during encoding. We address more specifically the case of Motion JPEG 2000. Simulation results show that the technique can be successfully applied to conceal information in regions of interest in the scene while providing with a good level of security. Furthermore, the scrambling is flexible and allows adjusting the amount of distortion introduced. This is achieved with a small impact on coding performance and negligible computational complexity increase. In the proposed video surveillance system, heterogeneous clients can remotely access the system through the Internet or 2G/3G mobile phone network. Thanks to the inherently scalable Motion JPEG 2000 codestream, the server is able to adapt the resolution and bandwidth of the delivered video depending on the usage environment of the client.

Protecting Privacy in Video Surveillance

2009

Forms of surveillance are very quickly becoming an integral part of crime control policy, crisis management, social control theory and community consciousness. In turn, it has been used as a simple and effective solution to many of these problems. However, privacy-related concerns have been expressed over the development and deployment of this technology. Used properly, video cameras help expose wrongdoing but typically come at the cost of privacy to those not involved in any maleficent activity. This work describes the design and implementation of a real-time, privacy-protecting video surveillance infrastructure that fuses additional sensor information (e.g. Radio-frequency Identification) with video streams and an access control framework in order to make decisions about how and when to display the individuals under surveillance. This video surveillance system is a particular instance of a more general paradigm of privacy-protecting data collection. In this paper we describe in detail the video processing techniques used in order to achieve real-time tracking of users in pervasive spaces while utilizing the additional sensor data provided by various instrumented sensors. In particular, we discuss background modeling techniques, object tracking and implementation techniques that pertain to the overall development of this system.

Privacy Protection in a Video Surveillance System

Protecting Privacy in Video Surveillance, 2009

This paper discusses a privacy-protected video surveillance system that makes use of JPEG extended range (JPEG XR). JPEG XR offers a low-complexity solution for the scalable coding of high-resolution images. To address privacy concerns, face regions are detected and scrambled in the transform domain, taking into account the quality and spatial scalability features of JPEG XR. Experiments were conducted to investigate the performance of our surveillance system, considering visual distortion, bit stream overhead, and security aspects. Our results demonstrate that subband-adaptive scrambling is able to conceal privacy-sensitive face regions with a feasible level of protection. In addition, our results show that subband-adaptive scrambling of face regions outperforms subband-adaptive scrambling of frames in terms of coding efficiency, except when low video bit rates are in use.

Privacy and Security in Video Surveillance

Intelligent Multimedia Surveillance, 2013

Video surveillance systems are usually installed to increase the safety and security of people or property in the monitored areas. Typical threat scenarios are robbery, vandalism, shoplifting or terrorism. Other application scenarios are more intimate and private such as home monitoring or assisted living. For a long time it was accepted that the potential benefits of video surveillance go hand in hand with a loss of personal privacy. However, with the on-board processing capabilities of modern embedded systems it becomes possible to compensate this privacy loss by making security and privacy protection inherent features of video surveillance cameras. In the first part of this chapter we motivate the need for the integration of security and privacy features, we discuss fundamental requirements and provide a comprehensive review of the state of the art. The second part presents the TrustCAM prototype system where a dedicated hardware security module is integrated into a camera system to achieve a high level of security. The chapter is concluded by a summary of open research issues and an outlook to future trends. 1 The Need for Security and Privacy Protection Reasons for deploying video surveillance systems are manifold. Frequently mentioned arguments are ensuring public safety, preventing vandalism and crime as well as investigating criminal offenses [40]. As part of that, cameras are often installed in public environments such as underground or train stations, in buses [39] or taxis [20], along roads and highways [8, 23], in sports stadiums or in shopping

Hiding privacy information in video surveillance system

2005

This paper proposes a detailed framework of storing privacy information in surveillance video as a watermark. Authorized personnel is not only removed from the surveillance video as in [1] but also embedded into the video itself, which can only be retrieved with a secrete key. A perceptual-model-based compressed domain video watermarking scheme is proposed to deal with the huge payload problem in the proposed surveillance system. A signature is also embedded into the header of the video as in [2] for authentication. Simulation results have shown that the proposed algorithm can embed all the privacy information into the video without affecting its visual quality. As a result, the proposed video surveillance system can monitor the unauthorized persons in a restricted environment, protect the privacy of the authorized persons but, at the same time, allow the privacy information to be revealed in a secure and reliable way.

Privacy information management for video surveillance

Biometric and Surveillance Technology for Human and Activity Identification X, 2013

The widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been proposed to automatically redact images of trusted individuals in the surveillance video. To identify these individuals for protection, the most reliable approach is to use biometric signals such as iris patterns as they are immutable and highly discriminative. In this paper, we propose a privacy data management system to be used in a privacy-aware video surveillance system. The privacy status of a subject is anonymously determined based on her iris pattern. For a trusted subject, the surveillance video is redacted and the original imagery is considered to be the privacy information. Our proposed system allows a subject to access her privacy information via the same biometric signal for privacy status determination. Two secure protocols, one for privacy information encryption and the other for privacy information retrieval are proposed. Error control coding is used to cope with the variability in iris patterns and efficient implementation is achieved using surrogate data records. Experimental results on a public iris biometric database demonstrate the validity of our framework.