Source identification of 3D printed objects based on inherent equipment distortion. Issue 82 (May 2019)

• Record Type: Journal Article
• Authors: Peng, Fei; Yang, Jing; Lin, Zi-Xing; Long, Min
• Is Part Of: Computers & security. Issue 82(2019)
• Page Start: 173
• Page End: 183
• Publication Date: 2019-05
• Subjects: Source identification -- 3D printed objects -- 3D printer -- Equipment distortion model -- Inherent equipment distortion; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2018.12.015
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: The widespread use of 3D printers introduces tremendous challenges for the regulation of illegal products. In the current situation, since it is impossible to completely prohibit users from using 3D printers to manufacture illegal products, source identification of 3D printed products is a possible alternative for regulators to trace the offenders. In this paper, a source identification scheme for 3D printed objects based on inherent equipment distortion is proposed. By investigating the 3D printing process, an equipment distortion model is constructed, and then the inherent equipment distortion is analyzed. Furthermore, in order to exhibit the inherent equipment distortion, a uniform mark is designed and the inherent equipment distortion is extracted. With the features of the inherent equipment distortion of the 3D printers, SVM classifier is employed for the source identification of the 3D printed objects. Experimental results and analysis show that it can obtain an average identification accuracy of 91.1% with the 3D printed objects from 9 printers, and the analysis also indicates that it can achieve satisfactory robustness and reliability.