TY - GEN
T1 - PrinTracker
T2 - 25th ACM Conference on Computer and Communications Security, CCS 2018
AU - Li, Zhengxiong
AU - Rathore, Aditya Singh
AU - Song, Chen
AU - Wei, Sheng
AU - Wang, Yanzhi
AU - Xu, Wenyao
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/15
Y1 - 2018/10/15
N2 - As 3D printing technology begins to outpace traditional manufacturing, malicious users increasingly have sought to leverage this widely accessible platform to produce unlawful tools for criminal activities. Therefore, it is of paramount importance to identify the origin of unlawful 3D printed products using digital forensics. Traditional countermeasures, including information embedding or watermarking, rely on supervised manufacturing process and are impractical for identifying the origin of 3D printed tools in criminal applications. We argue that 3D printers possess unique fingerprints, which arise from hardware imperfections during the manufacturing process, causing discrepancies in the line formation of printed physical objects. These variations appear repeatedly and result in unique textures that can serve as a viable fingerprint on associated 3D printed products. To address the challenge of traditional forensics in identifying unlawful 3D printed products, we present PrinTracker, the 3D printer identification system, which can precisely trace the physical object to its source 3D printer based on its fingerprint. Results indicate that PrinTracker provides a high accuracy using 14 different 3D printers. Under unfavorable conditions (e.g. restricted sample area, location and process), the PrinTracker can still achieve an acceptable accuracy of 92%. Furthermore, we examine the effectiveness, robustness, reliability and vulnerability of the PrinTracker in multiple real-world scenarios.
AB - As 3D printing technology begins to outpace traditional manufacturing, malicious users increasingly have sought to leverage this widely accessible platform to produce unlawful tools for criminal activities. Therefore, it is of paramount importance to identify the origin of unlawful 3D printed products using digital forensics. Traditional countermeasures, including information embedding or watermarking, rely on supervised manufacturing process and are impractical for identifying the origin of 3D printed tools in criminal applications. We argue that 3D printers possess unique fingerprints, which arise from hardware imperfections during the manufacturing process, causing discrepancies in the line formation of printed physical objects. These variations appear repeatedly and result in unique textures that can serve as a viable fingerprint on associated 3D printed products. To address the challenge of traditional forensics in identifying unlawful 3D printed products, we present PrinTracker, the 3D printer identification system, which can precisely trace the physical object to its source 3D printer based on its fingerprint. Results indicate that PrinTracker provides a high accuracy using 14 different 3D printers. Under unfavorable conditions (e.g. restricted sample area, location and process), the PrinTracker can still achieve an acceptable accuracy of 92%. Furthermore, we examine the effectiveness, robustness, reliability and vulnerability of the PrinTracker in multiple real-world scenarios.
KW - Embedded systems
KW - Forensics
KW - Manufacturing security
UR - http://www.scopus.com/inward/record.url?scp=85056877128&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056877128&partnerID=8YFLogxK
U2 - 10.1145/3243734.3243735
DO - 10.1145/3243734.3243735
M3 - Conference contribution
AN - SCOPUS:85056877128
T3 - Proceedings of the ACM Conference on Computer and Communications Security
SP - 1306
EP - 1323
BT - CCS 2018 - Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security
PB - Association for Computing Machinery
Y2 - 15 October 2018
ER -