| 词条 | Visual privacy |
| 释义 |
}} Visual Privacy is the relationship between collection and dissemination of visual information, the expectation of privacy, and the legal issues surrounding them. These days cameras are ubiquitous. They are one of the most common sensors found in electronic devices, ranging from smartphones to tablets, and laptops to surveillance cams. However, privacy and trust implications surrounding it limit its ability to seamlessly blend into our computing environment. In particular, large-scale camera networks have created increasing interest in understanding the advantages and disadvantages of such deployments. It is estimated that over 4 Million Cameras Deployed in the UK.[1] Due to increasing security concerns, camera networks have continued to proliferate across other countries such as the United States. While the impact of such systems continues to be evaluated, in parallel, tools for controlling how these camera networks are used and modifications to the images or video sent to end-users have been explored. TechnologiesTo enhance visual privacy, a number of different technologies have been suggested. Forms of Visual DataVisual Privacy is often typically applied to particular technologies including:
SystemsMany different forms of technologies are explored to preserve privacy while providing information collected from camera networks. Most of these solutions rely upon the target application and try to accomplish it in a privacy preserving manner:
Visual privacy hence encompasses privacy aware and privacy preserving systems which factor in the compute design choices,[5] privacy policies regarding data-sharing in a collaborative and distributive environment and data ownership itself. At times privacy and trust are interlinked especially for the adoption and wide-scale acceptance of any technology. Having a fair and accurate computer vision model goes a long way into ensuring the prior two. A lot of developers are also now inculcating perspectives from Privacy by design. These include but are not limited to processing all user sensitive data on the edge client device, decreasing data retentivity, and ensuring that the data is not used for anything it is not intended for. References1. ^McCahill, M. and Norris, C. 2004, From cameras to control rooms: the mediation of the image by cctv operatives, CCTV and Social Control: The politics and practice of video surveillance-European and global perspectives, 2004 2. ^Respectful Cameras: Detecting Visual Markers in Real-Time to Address Privacy Concerns. Jeremy Schiff, Marci Meingast, Deirdre K. Mulligan, Shankar Sastry, and Ken Goldberg. International Conference on Intelligent Robots and Systems (IROS). San Diego, California. October 2007. 3. ^Cardea: Context-Aware Visual Privacy Protection for Photo Taking and Sharing. Jiayu Shu, Rui Zheng, and Pan Hui. In Proceedings of ACM Multimedia Systems (MMSys 2018), Amsterdam Netherlands, June 2018. 4. ^{{Cite journal|last=Pittaluga|first=Francesco|last2=Koppal|first2=Sanjeev J.|date=June 2015|title=Privacy preserving optics for miniature vision sensors|url=http://ieeexplore.ieee.org/document/7298628/|journal=2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)|location=Boston, MA, USA|publisher=IEEE|pages=314–324|doi=10.1109/CVPR.2015.7298628|isbn=9781467369640}} 5. ^{{Cite journal|last=Koelle|first=Marion|last2=Wolf|first2=Katrin|last3=Boll|first3=Susanne|date=2018|title=Beyond LED Status Lights - Design Requirements of Privacy Notices for Body-worn Cameras|url=http://dl.acm.org/citation.cfm?doid=3173225.3173234|journal=Proceedings of the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction - TEI '18|location=Stockholm, Sweden|publisher=ACM Press|pages=177–187|doi=10.1145/3173225.3173234|isbn=9781450355681}} External links
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