Printer Source Identification Based on Graph Model
Authors: 
Rui Tian, Ziqi ZhuAuthors Info & Claims
Pages 125 - 131
Abstract
Printer source identification is an important means of document inspection and plays an important role in forensic identification. In the research of printer source recognition, traditional methods basically rely on specific characters to recognize printed documents, but the recognition of Chinese printed documents is usually difficult because there are few or no specific characters. In view of this situation, this paper proposes a text-independent printer source identification method, which uses a graphical model to model the timing relationship of the printer, and then extracts the timing characteristics of the printer, which belong to the text-independent printer. Internal features, so that the method can be recognized without relying on specific characters, and has achieved good experimental results. Experimental data show that the proposed method is very useful for the traceability of printed documents.
References
[1]
Li Wei,Li Jin,Zhang Maolin.Analysis of the evidential value of document examination conclusions and their realization[J].Rule of Law and Society,2020(33):81-82.
[2]
Hao Jianyuan. Printed document source forensics based on text line geometric distortion[D].Dalian University of Technology,2016.
[3]
Hao J, Kong X, Shang S (2015) Printer identification using page geometric distortion on text lines. In: China summit and international conference on signal and information processing. IEEE, pp 856–860
[4]
J. E. Girard, Criminalistics: Forensic Science, Crime, And Terrorism, 3rd ed. Burlington, MA: Jones & Bartlett Publishers, 2013.
[5]
Merrill RA,Bartick EG, Iii J . Forensic discrimination of photocopy and printer toners I. The development of an infrared spectral library[J]. Analytical and Bioanalytical Chemistry, 2003.
[6]
Shaffer DK. Forensic document analysis using scanning microscopy[J]. Proceedings of SPIE - The International Society for Optical Engineering, 2009.
[7]
A. K. Mikkilineni, P.-J. Chiang, G. N. Ali, G. T. C. Chiu, J. P. Allebach, and E. J. Delp, “Printer Identification Based On Graylevel Co-occurrence Features For Security And Forensic Applications,” in Proc. SPIE Int. Conf. on Security, Steganography, and Watermarking of Multimedia Contents VII, vol. 5681, San Jose, CA, March 2005, pp. 430–440.
[8]
A. Ferreira, L. Bondi, L. Baroffio, P. Bestagini, J. Huang, J. dos Santos, S. Tubaro, and A. Rocha, “Data-Driven Feature Characterization Techniques for Laser Printer Attribution,” IEEE Trans. Inf. Forensics Security, vol. 12, no. 8, pp. 1860–1873, 2017
[9]
A.K.Mikkilineni, N. Khanna, and E. J. Delp, “Forensic Printer Detection Using Intrinsic Signatures.” in Media Forensics and Security, 2011, p. 78800R.
[10]
M. J. Tsai, J. S. Yin, I. Yuadi, and J. Liu, “Digital Forensics of Printed Source Identification for Chinese Characters,” Multimedia Tools and Applications, vol. 73, no. 3, pp. 2129–2155, Dec 2014.
[11]
Joshi S, Lomba M, Goyal V, Augmented Data and Improved Noise Residual-Based CNN for Printer Source Identification[C]// ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018.
[12]
Mikkilineni A K, Chiang P J, Ali G N, Printer identification based on graylevel co-occurrence features for security and forensic applications[C]// Security, Steganography, and Watermarking of Multimedia Contents VII, San Jose, California, USA, January 17-20, 2005, Proceedings. DBLP, 2005.
[13]
Mikkilineni A K, Arslan O, Chiang P J, Printer Forensics Using SVM Techniques[C]// NIP & Digital Fabrication Conference. 2005.
[14]
A. Ferreira, L. C. Navarro, G. Pinheiro, J. A. dos Santos, and A. Rocha, “Laser Printer Attribution: Exploring New Features And Beyond,” Forensic Science International, vol. 247, pp. 105–125, 2015.
[15]
Tsai M J, Hsu C L, Yin J S, Digital forensics for printed character source identification[C]// IEEE International Conference on Multimedia & Expo. IEEE, 2016.
[16]
Tsai M J, Hsu C L, Yin J S, Japanese Character based Printed Source Identification *[C]// Circuits and Systems (ISCAS), 2015 IEEE International Symposium on. IEEE, 2015.
[17]
Joshi S, Saxena S, Khanna N . Source Printer Identification from Document Images Acquired using Smartphone[J]. 2020.
[18]
Y. Wu, X. Kong, X. You, and Y. Guo, “Printer Forensics Based on Page Document's Geometric Distortion,” in Proc. IEEE International Conference on Image Processing (ICIP). IEEE, 2009, pp. 2909–2912.
[19]
Yao B, Fei-Fei L. Modeling mutual context of object and human pose in human-object interaction activities[C]:2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010: 17-24.
[20]
Yao J, Fidler S, Urtasun R. Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation[C]:2012 IEEE conference on computer vision and pattern recognition. IEEE, 2012: 702-709.
[21]
Santoro A, Raposo D, Barrett D G T, A simple neural network module for relational reasoning[J]. arXiv preprint arXiv:1706.01427, 2017.
[22]
Yatskar M, Zettlemoyer L, Farhadi A. Situation recognition: Visual semantic role labeling for image understanding[C]:Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 5534-5542.
[23]
Krähenbühl P, Koltun V. Efficient inference in fully connected crfs with gaussian edge potentials[J]. Advances in neural information processing systems, 2011, 24: 109-117.
[24]
Liu S, De Mello S, Gu J, Learning affinity via spatial propagation networks[J]. arXiv preprint arXiv:1710.01020, 2017.
[25]
Scarselli F, Gori M, Tsoi A C, The graph neural network model[J]. IEEE transactions on neural networks, 2008, 20(1): 61-80.
[26]
Kipf T N,Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907, 2016
[27]
K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. In ECCV, 2016.
[28]
S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He. Aggregated residual transformations for deep neural networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5987–5995. IEEE, 2017
[29]
Jie H, Li S, Gang S, Squeeze-and-Excitation Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, PP(99).
[30]
Woo S, Park J, Lee J Y, CBAM: Convolutional Block Attention Module[J]. Springer, Cham, 2018.
Index Terms
- Printer Source Identification Based on Graph Model
Recommendations
Printer Identification Based on Distance Transform
ICINIS '08: Proceedings of the 2008 First International Conference on Intelligent Networks and Intelligent SystemsPrinted document examination plays an important role in public security. A printer identification system is presented for the purpose of identifying the printer which created a suspect printed document. It is composed of image acquisition, image ...
Source printer identification using printer specific pooling of letter descriptors
AbstractThe digital revolution has replaced the use of printed documents with their digital counterparts. However, many applications require the use of both due to several factors, including challenges of digital security, installation costs, ...
Highlights- Extensive analysis of printer signature variations across a printed document.
- ...
Laser Printer Fuzzy Identification Based on Correlative Specific Area of Character Image
CIS '07: Proceedings of the 2007 International Conference on Computational Intelligence and SecurityThe type of printer needs to be identified in many cases as evidence. This paper proposed a new method to identify the type of Laser printer according to the feature of printed character image. Character image is segmented into 16 equal-area quadrate ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
January 2023
219 pages
ISBN:9781450398633
DOI:10.1145/3583788
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 June 2023
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICMLSC 2023
ICMLSC 2023: 2023 The 7th International Conference on Machine Learning and Soft Computing
January 5 - 7, 2023
Chongqing, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 13Total Downloads
- Downloads (Last 12 months)6
- Downloads (Last 6 weeks)0
Reflects downloads up to 02 Oct 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format