Next Article in Journal
An Extended Step-Wise Weight Assessment Ratio Analysis with Symmetric Interval Type-2 Fuzzy Sets for Determining the Subjective Weights of Criteria in Multi-Criteria Decision-Making Problems
Next Article in Special Issue
A Watermarking Method for 3D Printing Based on Menger Curvature and K-Mean Clustering
Previous Article in Journal
Automatic Generation of Dynamic Skin Deformation for Animated Characters
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(4), 90; https://doi.org/10.3390/sym10040090

Anti-3D Weapon Model Detection for Safe 3D Printing Based on Convolutional Neural Networks and D2 Shape Distribution

1
Department of IT Convergence & Application Engineering, Pukyong National University, Busan 608-737, Korea
2
Department of Information Security, Tongmyong University, Busan 608-711, Korea
*
Author to whom correspondence should be addressed.
Received: 28 February 2018 / Revised: 21 March 2018 / Accepted: 29 March 2018 / Published: 31 March 2018
(This article belongs to the Special Issue Information Technology and Its Applications 2018)
Full-Text   |   PDF [56204 KB, uploaded 3 May 2018]   |  

Abstract

With the development of 3D printing, weapons are easily printed without any restriction from the production managers. Therefore, anti-3D weapon model detection is necessary issue in safe 3D printing to prevent the printing of 3D weapon models. In this paper, we would like to propose an anti-3D weapon model detection algorithm to prevent the printing of anti-3D weapon models for safe 3D printing based on the D2 shape distribution and an improved convolutional neural networks (CNNs). The purpose of the proposed algorithm is to detect anti-3D weapon models when they are used in 3D printing. The D2 shape distribution is computed from random points on the surface of a 3D weapon model and their geometric features in order to construct a D2 vector. The D2 vector is then trained by improved CNNs. The CNNs are used to detect anti-3D weapon models for safe 3D printing by training D2 vectors which have been constructed from the D2 shape distribution of 3D weapon models. Experiments with 3D weapon models proved that the D2 shape distribution of 3D weapon models in the same class is the same. Training and testing results also verified that the accuracy of the proposed algorithm is higher than the conventional works. The proposed algorithm is applied in a small application, and it could detect anti-3D weapon models for safe 3D printing. View Full-Text
Keywords: 3D printing security; safe 3D printing; 3D weapon model; D2 shape distribution; convolutional neural networks 3D printing security; safe 3D printing; 3D weapon model; D2 shape distribution; convolutional neural networks
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Pham, G.N.; Lee, S.-H.; Kwon, O.-H.; Kwon, K.-R. Anti-3D Weapon Model Detection for Safe 3D Printing Based on Convolutional Neural Networks and D2 Shape Distribution. Symmetry 2018, 10, 90.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top