Paper Info
Title
HEHLKAPPE: Utilizing Deep Learning to Manipulate Surveillance Camera Footage in Real-Time
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.
Year
DOI
Venue
2019
10.1145/3339252.3340102
Proceedings of the 14th International Conference on Availability, Reliability and Security
Keywords
Field
DocType
Computer Vision, Embedded Device, Firmware, Man in the Middle, Neural Networks, Object Detection
Data mining,Computer science,Surveillance camera,Human–computer interaction,Artificial intelligence,Deep learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-7164-3
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Alexander Aigner100.34
Rene Zeller200.34