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

Rory Smith*, Tilo Burghardt

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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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.
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
PublisherSpringer, Cham
Number of pages16
ISBN (Electronic)9783030129392
ISBN (Print)9783030129385
Publication statusPublished - 14 Feb 2019
Event40th German Conference on Pattern Recognition, GCPR 2018 - Stuttgart, Germany
Duration: 9 Oct 201812 Oct 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11269 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference40th German Conference on Pattern Recognition, GCPR 2018


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