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DeepKey: Towards End-to-End Physical Key Replication From a Single Photograph

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationPattern Recognition
Subtitle of host publication40th German Conference, GCPR 2018, Stuttgart, Germany, October 9-12, 2018, Proceedings
EditorsThomas Brox, Andrés Bruhn, Mario Fritz
Publisher or commissioning bodySpringer, Cham
Pages487-502
Number of pages16
ISBN (Electronic)978-3-030-12939-2
ISBN (Print)978-3-030-12938-5
DOIs
DateAccepted/In press - 27 Aug 2018
DateE-pub ahead of print (current) - 14 Feb 2019

Publication series

Name
ISSN (Print)0302-9743

Abstract

This paper describes DeepKey, an end-to-end deep neural architecture capable of taking a digital RGB image of an ‘everyday’ scene containing a pin tumbler key (e.g. lying on a table or carpet) and fully automatically inferring a printable 3D key model. We report on the key detection performance and describe how candidates can be transformed into physical prints. We show an example opening a real-world lock. Our system is described in detail, providing a breakdown of all components including key detection, pose normalisation, bitting segmentation and 3D model inference. We provide an in-depth evaluation and conclude by reflecting on limitations, applications, potential security risks and societal impact. We contribute the DeepKey Datasets of 5,300+ images covering a few test keys with bounding boxes, pose and unaligned mask data.

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Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Springer Nature at https://link.springer.com/chapter/10.1007%2F978-3-030-12939-2_34. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 5 MB, PDF-document

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