Hehlkappe: Utilizing deep learning to manipulate surveillance camera footage in real-time

Alexander Aigner, Rene Zeller

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

2 Citations (Scopus)

Abstract

Image analysis and manipulation have always been active topics, both in practice and academia. Driven by the progress in the field of deep learning, significant advances have been achieved in recent years. This causes even complex image manipulation and analysis tasks to be easily applicable by a wide audience. Combined with traditional attack methods, this results in new attack vectors. In this paper, we present HEHLKAPPE, an application to hide persons from real-time video streams while keeping other movement untouched. The application is fully automated and does not require any domain knowledge in deep learning, image manipulation or other related areas in order to use it. In addition, we present 2 attack vectors to access the video stream to enable the manipulation. Our evaluation shows that HEHLKAPPE works well with static camera positions, is able to adapt to background changes and therefore is suitable to deceive observers. The discussion of the results discovers potential for improvement by using even more sophisticated techniques. We are confident that these techniques will be applicable in real-time in the near future. Appropriate countermeasures to mitigate our attack include improving the state of IoT security and verifying the authenticity of each frame using a blockchain-linke structure.

Original languageEnglish
Title of host publicationProceedings of the 14th International Conference on Availability, Reliability and Security, ARES 2019
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Electronic)9781450371643
ISBN (Print)978-1-4503-7164-3
DOIs
Publication statusPublished - 26 Aug 2019
Event14th International Conference on Availability, Reliability and Security, ARES 2019 - Canterbury, United Kingdom
Duration: 26 Aug 201929 Aug 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference14th International Conference on Availability, Reliability and Security, ARES 2019
CountryUnited Kingdom
CityCanterbury
Period26.08.201929.08.2019

Keywords

  • Computer Vision
  • Embedded Device
  • Firmware
  • Man in the Middle
  • Neural Networks
  • Object Detection

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