Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing

Xiaolong Ma, Zhe Li, Hongjia Li, Qiyuan An, Qinru Qiu, Wenyao Xu, Yanzhi Wang

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

Abstract

Increasing malicious users have sought practices to leverage 3D printing technology to produce unlawful tools in criminal activities. It is of vital importance to enable 3D printers to identify the objects to be printed and terminate at early stage if illegal objects are identified. Deep learning yields significant rises in performance in the object recognition tasks. However, the lack of large-scale databases in 3D printing domain stalls the advancement of automatic illegal weapon recognition. This paper presents a new 3D printing image database, namely C3PO, which compromises two subsets for the different system working scenarios. We extract images from the numerical control programming code files of 22 3D models, and then categorize the images into 10 distinct labels. These two sets are designed for identifying: (i). printing knowledge source (G-code) at beginning of manufacturing, (ii). printing procedure during manufacturing. Importantly, we demonstrate that the weapons can be recognized in either scenario using deep learning based approaches using our proposed database. The quantitative results are promising, and the future exploration of the database and the crime prevention in 3D printing are demanding tasks.

Original languageEnglish (US)
Title of host publicationASP-DAC 2020 - 25th Asia and South Pacific Design Automation Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages494-499
Number of pages6
ISBN (Electronic)9781728141237
DOIs
StatePublished - Jan 2020
Event25th Asia and South Pacific Design Automation Conference, ASP-DAC 2020 - Beijing, China
Duration: Jan 13 2020Jan 16 2020

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
Volume2020-January

Conference

Conference25th Asia and South Pacific Design Automation Conference, ASP-DAC 2020
CountryChina
CityBeijing
Period1/13/201/16/20

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing'. Together they form a unique fingerprint.

  • Cite this

    Ma, X., Li, Z., Li, H., An, Q., Qiu, Q., Xu, W., & Wang, Y. (2020). Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing. In ASP-DAC 2020 - 25th Asia and South Pacific Design Automation Conference, Proceedings (pp. 494-499). [9045180] (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC; Vol. 2020-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASP-DAC47756.2020.9045180