Safeguarding National Security Interests Utilizing Location-Aware Camera Devices

• URL: http://arxiv.org/abs/2205.03330v1
• Date: Fri, 6 May 2022 16:06:37 GMT
• Title: Safeguarding National Security Interests Utilizing Location-Aware Camera Devices
• Authors: Sreejith Gopinath and Aspen Olmsted
• Abstract summary: We propose a Global Positioning System-based approach to restrict the ability of smart cameras to capture and store images of sensitive areas. Our work proposes a Global Positioning System-based approach to restrict the ability of smart cameras to capture and store images of sensitive areas.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid advancement of technology has resulted in advanced camera capabilities coming to smaller form factors with improved energy efficiency. These improvements have led to more efficient and capable cameras on mobile devices like mobile phones, tablets, and even eyeglasses. Using these unobtrusive cameras, users can capture photographs and videos of almost any location where they have physical access. Unfortunately, the proliferation of highly compact cameras has threatened the privacy rights of individuals and even entire nations and governments. For example, governments may not want photographs or videos of sensitive installations or locations like airside operations of military bases or the inner areas of nuclear power plants to be captured for unapproved uses. In addition, solutions that obfuscate images in post-processing are subject to threats that could siphon unprocessed data. Our work proposes a Global Positioning System-based approach to restrict the ability of smart cameras to capture and store images of sensitive areas.

Related papers

• Privacy-Preserving Representations are not Enough -- Recovering Scene Content from Camera Poses [63.12979986351964]
  Existing work on privacy-preserving localization aims to defend against an attacker who has access to a cloud-based service. We show that an attacker can learn about details of a scene without any access by simply querying a localization service.
  arXiv  Detail & Related papers  (2023-05-08T10:25:09Z)
• Privacy-Preserving Visual Localization with Event Cameras [13.21898697942957]
  Event cameras can potentially make robust localization due to high dynamic range and small motion blur. We propose applying event-to-image conversion prior to localization which leads to stable localization. In the privacy perspective, event cameras capture only a fraction of visual information compared to normal cameras.
  arXiv  Detail & Related papers  (2022-12-04T07:22:17Z)
• Learning Active Camera for Multi-Object Navigation [94.89618442412247]
  Getting robots to navigate to multiple objects autonomously is essential yet difficult in robot applications. Existing navigation methods mainly focus on fixed cameras and few attempts have been made to navigate with active cameras. In this paper, we consider navigating to multiple objects more efficiently with active cameras.
  arXiv  Detail & Related papers  (2022-10-14T04:17:30Z)
• Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities [66.4525391417921]
  We design and evaluate a multi-sensor drone detection system. Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest. The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution.
  arXiv  Detail & Related papers  (2022-07-05T10:00:58Z)
• Lasers to Events: Automatic Extrinsic Calibration of Lidars and Event Cameras [67.84498757689776]
  This paper presents the first direct calibration method between event cameras and lidars. It removes dependencies on frame-based camera intermediaries and/or highly-accurate hand measurements.
  arXiv  Detail & Related papers  (2022-07-03T11:05:45Z)
• PrivHAR: Recognizing Human Actions From Privacy-preserving Lens [58.23806385216332]
  We propose an optimizing framework to provide robust visual privacy protection along the human action recognition pipeline. Our framework parameterizes the camera lens to successfully degrade the quality of the videos to inhibit privacy attributes and protect against adversarial attacks.
  arXiv  Detail & Related papers  (2022-06-08T13:43:29Z)
• Deep Learning Approach Protecting Privacy in Camera-Based Critical Applications [57.93313928219855]
  We propose a deep learning approach towards protecting privacy in camera-based systems. Our technique distinguishes between salient (visually prominent) and non-salient objects based on the intuition that the latter is unlikely to be needed by the application.
  arXiv  Detail & Related papers  (2021-10-04T19:16:27Z)
• Can poachers find animals from public camera trap images? [14.61316451496861]
  We investigate the robustness of geo-obfuscation for maintaining camera trap location privacy. Simple intuitives and publicly available satellites can be used to reduce the area likely to contain the camera by 87%.
  arXiv  Detail & Related papers  (2021-06-21T16:31:47Z)
• How low can you go? Privacy-preserving people detection with an omni-directional camera [2.433293618209319]
  In this work, we use a ceiling-mounted omni-directional camera to detect people in a room. This can be used as a sensor to measure the occupancy of meeting rooms and count the amount of flex-desk working spaces available.
  arXiv  Detail & Related papers  (2020-07-09T10:10:23Z)
• Towards large-scale, automated, accurate detection of CCTV camera objects using computer vision. Applications and implications for privacy, safety, and cybersecurity. (Preprint) [0.7349727826230861]
  We introduce CCTVCV -- the first and only computer vision MS-compatible models that are able to accurately detect CCTV and video surveillance cameras in images and video frames. To this end, our best detectors were built using 8387 images that were manually reviewed and annotated to contain 10419 CCTV camera instances.
  arXiv  Detail & Related papers  (2020-06-06T13:49:09Z)
• Privacy-Protection Drone Patrol System based on Face Anonymization [1.20050068684031]
  This work proposes face-anonymizing drone patrol system. One person's face in a video is transformed into a different face with facial components maintained. Our system is evaluated with a customized drone consisting of a high-resolution camera, a companion computer, and a drone control computer.
  arXiv  Detail & Related papers  (2020-05-29T05:14:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.