An Enhanced Document Source Identification System for Printer Forensic Applications based on the Boosted Quantum KNN Classifier
Received: 26 October 2024 | Revised: 4 December 2024 | Accepted: 14 December 2024 | Online: 22 December 2024
Corresponding author: Wisal Hashim Abdulsalam
Abstract
Document source identification in printer forensics involves determining the origin of a printed document based on characteristics such as the printer model, serial number, defects, or unique printing artifacts. This process is crucial in forensic investigations, particularly in cases involving counterfeit documents or unauthorized printing. However, consistent pattern identification across various printer types remains challenging, especially when efforts are made to alter printer-generated artifacts. Machine learning models are often used in these tasks, but selecting discriminative features while minimizing noise is essential. Traditional KNN classifiers require a careful selection of distance metrics to capture relevant printing characteristics effectively. This study proposes leveraging quantum-inspired computing to improve KNN classifiers for printer source identification, offering better accuracy even with noisy or variable printing conditions. The proposed approach uses the Gray Level Co-occurrence Matrix (GLCM) for feature extraction, which is resilient to changes in rotation and scale, making it well-suited for texture analysis. Experimental results show that the quantum-inspired KNN classifier captures subtle printing artifacts, leading to improved classification accuracy despite noise and variability.
Keywords:
printer forensics, document source identification, quantum-inspired computing, feature modelingDownloads
References
H. Joren, O. Gupta, and D. Raviv, "Printing and scanning investigation for image counter forensics," EURASIP Journal on Image and Video Processing, vol. 2022, no. 1, Feb. 2022, Art. no. 2.
M. Kumar, S. Gupta, and N. Mohan, "A computational approach for printed document forensics using SURF and ORB features," Soft Computing, vol. 24, no. 17, pp. 13197–13208, Sep. 2020.
R. Hamzehyan, F. Razzazi, and A. Behrad, "Printer source identification by feature modeling in the total variable printer space," Journal of Forensic Sciences, vol. 66, no. 6, pp. 2261–2273, 2021.
Y. F. Chen, H. H. Kao, and C. P. Yen, "An Application of Deep Learning Technology in The Recognition of Forged Documents with Color Laser Printing," Journal of Computers, vol. 34, no. 5, pp. 135–147, Oct. 2023.
Y. Liu et al., "Inkjet printer prediction under complicated printing conditions based on microscopic image features," Science & Justice, vol. 64, no. 3, pp. 269–278, May 2024.
N. F. E. Abady, H. H. Zayed, and M. Taha, "An Efficient Source Printer Identification Model using Convolution Neural Network (SPI-CNN)," International Journal of Advanced Computer Science and Applications, vol. 14, no. 3, 2023.
R. Tian and Z. Zhu, "Printer Source Identification Based on Graph Model," in 2023 The 7th International Conference on Machine Learning and Soft Computing (ICMLSC), Chongqing China, Jan. 2023, pp. 125–131.
M. J. Tsai and I. Yuadi, "Source Identification for Printed Arabic Characters," in Proceedings of the 9th IEEE International Conference on Ubi-Media Computing, 2016, pp. 49–53.
H. M. Al-Barhamtoshy, K. M. Jambi, S. M. Abdou, and M. A. Rashwan, "Arabic Documents Information Retrieval for Printed, Handwritten, and Calligraphy Image," IEEE Access, vol. 9, pp. 51242–51257, 2021.
W. Abdulsalam, S. Mashhadani, S. Hussein, and A. Hashim, "Artificial Intelligence Techniques to Identify Individuals through Palm Image Recognition," International Journal of Mathematics and Computer Science, vol. 20, no. 1, pp. 165–171, 2024.
H. M. Al-Dabbas, R. A. Azeez, and A. E. Ali, "Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13706–13713, Apr. 2024.
W. H. Abdulsalam, R. S. Alhamdani, and M. N. Abdullah, "Speech Emotion Recognition Using Minimum Extracted Features," in 2018 1st Annual International Conference on Information and Sciences (AiCIS), Fallujah, Iraq, Nov. 2018, pp. 58–61.
R. H. Ali and W. H. Abdulsalam, "Attention-Deficit Hyperactivity Disorder Prediction by Artificial Intelligence Techniques," Iraqi Journal of Science, pp. 5281–5294, Sep. 2024.
M. Açıkkar and S. Tokgöz, "An improved KNN classifier based on a novel weighted voting function and adaptive k-value selection," Neural Computing and Applications, vol. 36, no. 8, pp. 4027–4045, Mar. 2024, https://doi.org/10.1007/s00521-023-09272-8.
E. E. Miandoab and F. S. Gharehchopogh, "A Novel Hybrid Algorithm for Software Cost Estimation Based on Cuckoo Optimization and K-Nearest Neighbors Algorithms," Engineering, Technology & Applied Science Research, vol. 6, no. 3, pp. 1018–1022, Jun. 2016.
J. Li, J. Zhang, J. Zhang, and S. Zhang, "Quantum KNN Classification With K Value Selection and Neighbor Selection," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 43, no. 5, pp. 1332–1345, Feb. 2024.
P. Gonasagi, S. S. Rumma, and M. Hangarge, "Text-Independent Source Identification of Printed Documents using Texture Features and CNN Model," presented at the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022), Aug. 2023, pp. 250–261.
B. Belarbi, M. E. A. Ghernaout, and T. Benabdallah, "Implementation of a New Geometrical Qualification (DQ) Method for an Open Access Fused Filament Fabrication 3D Printer," Engineering, Technology & Applied Science Research, vol. 9, no. 3, pp. 4182–4187, Jun. 2019.
M. J. Tsai and I. Yuadi, "Digital forensics of microscopic images for printed source identification," Multimedia Tools and Applications, vol. 77, no. 7, pp. 8729–8758, Apr. 2018.
J. Lee, H. Kim, S. Yook, and T. Y. Kang, "Identification of document paper using hybrid feature extraction," Journal of Forensic Sciences, vol. 68, no. 5, pp. 1808–1815, 2023.
D. T. Nguyen, P. Q. Nguyen, and H. B. A. Mai, "Analysis of printed document identification based on Deep Learning," CTU Journal of Innovation and Sustainable Development, vol. 15, no. Special issue: ISDS, pp. 119–125, Oct. 2023.
Z. Li and Q. Peng, "Local Polar Pattern for Source Color Laser Printer Identification," IEEE Access, vol. 12, pp. 83377–83390, 2024.
Z. Guo, S. Wang, Z. Zheng, and K. Sun, "Printer source identification of quick response codes using residual attention network and smartphones," Engineering Applications of Artificial Intelligence, vol. 131, May 2024, Art. no. 107822.
Q. P. Nguyen, N. T. Dang, A. Mai, and V. S. Nguyen, "Features Selection in Microscopic Printing Analysis for Source Printer Identification with Machine Learning," in Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications, 2021, pp. 210–223.
M. N. M. Asri, N. F. Nestrgan, N. A. M. Nor, and R. Verma, "On the discrimination of inkjet, laser and photocopier printed documents using Raman spectroscopy and chemometrics: Application in forensic science," Microchemical Journal, vol. 165, Jun. 2021, Art. no. 106136.
Q.-T. Nguyen, A. Mai, L. Chagas, and N. Reverdy-Bruas, "Microscopic printing analysis and application for classification of source printer," Computers & Security, vol. 108, Sep. 2021, Art. no. 102320.
S. Joshi, Y. K. Gupta, and N. Khanna, "Source printer identification using printer specific pooling of letter descriptors," Expert Systems with Applications, vol. 192, Apr. 2022, Art. no. 116344.
M. Bibi, A. Hamid, M. Moetesum, and I. Siddiqi, "Document forgery detection using source printer identification: A comparative study of text-dependent versus text-independent analysis," Expert Systems, vol. 39, no. 8, 2022, Art. no. e13020.
P. Q. Vo et al., "Auto Machine Learning-Based Approach for Source Printer Identification," in Recent Challenges in Intelligent Information and Database Systems, Ho Chi Minh City, Vietnam, 2022, pp. 668–680.
N. F. El Abady, M. Taha, and H. H. Zayed, "Text-Independent Algorithm for Source Printer Identification Based on Ensemble Learning," Computers, Materials & Continua, vol. 73, no. 1, pp. 1417–1436, 2022.
D.-G. Kim, J.-U. Hou, and H.-K. Lee, "Learning deep features for source color laser printer identification based on cascaded learning," Neurocomputing, vol. 365, pp. 219–228, Nov. 2019.
P. Dehbozorgi, O. Ryabchykov, and T. Bocklitz, "A Systematic Investigation of Image Pre-Processing on Image Classification," IEEE Access, vol. 12, pp. 64913–64926, 2024.
M. J. Manaa, A. R. Abbas, and W. A. Shakur, "Improving the Resolution of Images Using Super-Resolution Generative Adversarial Networks," in Artificial Intelligence, Data Science and Applications, 2024, pp. 68–77.
G. Prasad, V. S. Gaddale, R. C. Kamath, V. J. Shekaranaik, and S. P. Pai, "A Study of Dimensionality Reduction in GLCM Feature-Based Classification of Machined Surface Images," Arabian Journal for Science and Engineering, vol. 49, no. 2, pp. 1531–1553, Feb. 2024.
J. Rout, S. K. Das, P. Mohalik, S. Mohanty, C. K. Mohanty, and S. K. Behera, "GLCM Based Feature Extraction and Medical X-ray Image Classification Using Machine Learning Techniques," in Intelligent Systems and Machine Learning, Hyderabad, India, 2023, pp. 52–63.
S. Joshi, S. Saxena, and N. Khanna, "Source printer identification from document images acquired using smartphone," Journal of Information Security and Applications, vol. 84, Aug. 2024, Art. no. 103804.
E. Zardini, E. Blanzieri, and D. Pastorello, "A quantum k-nearest neighbors algorithm based on the Euclidean distance estimation," Quantum Machine Intelligence, vol. 6, no. 1, Apr. 2024, Art. no. 23.
F. Kamalov, S. Elnaffarr, A. Cherukuri, and A. Jonnalagadda, "Forward feature selection: empirical analysis," Journal of Intelligent Systems and Internet of Things, vol. 11, no. 1, pp. 44–54, 2024.
W. Chen and J. Li, "SIGAN-CNN: Convolutional Neural Network Based Stepwise Improving Generative Adversarial Network for Time Series Classification of Small Sample Size," IEEE Access, vol. 12, pp. 85499–85510, 2024.
S. M. Darwish and H. M. ELgohary, "Building an expert system for printer forensics: A new printer identification model based on niching genetic algorithm," Expert Systems, vol. 38, no. 2, 2021, Art. no. e12624.
R. Divya and J. Dinesh Peter, "Quantum Machine Learning: A comprehensive review on optimization of machine learning algorithms," in 2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS), Kollam, India, Nov. 2021, pp. 1–6.
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Copyright (c) 2024 Shahlaa Mashhadani, Wisal Hashim Abdulsalam, Iptehaj Alhakam, Oday Ali Hassen, Saad M. Darwish
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