Visual privacy protection methods: A survey

Abstract

Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambientassisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual's privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users' acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people's privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.

De-identification for privacy protection in multimedia content: A survey

“…The unsolved problems are the detection of faces in crowd scenes and real-time de-identification. Privacy might be compromised in video sequences if the face detection algorithm fails in a single frame, so one of the directions of research is the development of robust and effective algorithms for privacy protection that can efficiently cope with situations when computer vision algorithms fail [38,200].…”

“…A valuable review of visual privacy and visual privacy protection methods is given in [38]. Authors classified the methods for privacy protection of individuals appearing in videos into five large categories: (i) intervention -preventing someone to capture private visual data from the environment; (ii) blind vision -image or video processing in an anonymous way; (iii) secure processingprocess visual information in a privacy respectful way; (iv) redaction -methods based on image filtration, encryption and ksame family algorithms, object/people removal, visual abstraction/ object replacement, and (v) data hiding -steganography and watermarking-based methods.…”

“…In either case, the de-identification process is required to be of sufficient effectiveness, regardless of whether the recognition attempts are made by humans or by machines. Moreover, in many cases, the process of de-identification has also to preserve the data utility, naturalness and intelligibility [37,38].…”

Privacy Protection in Street-View Panoramas Using Depth and Multi-View Imagery

“…Privacy protection Several approaches have been proposed for privacy protection in street-view imagery [5,6,7,8,9]. The most common way to hide sensitive information is to detect the objects of interest and blur them [10].…”

Privacy protection vs. utility in visual data

“…In a recent workshop dedicated protection methods have been proposed in order to solve the specified visual privacy task [5,19,20,30,34,40,41,49]. A comprehensive discussion on the state of the art in this field can be found in the surveys of Winkler et al [58] and Padilla-López et al [39].…”