Analysis of printed document identification based on Deep Learning
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Abstract
In this study, we investigate the effectiveness of ResNet, a deep neural network architecture, for a deep learning approach to address the problem of printed document identification. ResNet is known for its ability to handle the vanishing gradient problem and learn highly representative features. Multiple variations of ResNet have been applied, including ResNet50, ResNet101, and ResNet152, which provide the backbone architecture of our classification model and are trained on a comprehensive dataset of microscopic printed images containing some microscopic printing patterns from various source printers. We also incorporate Mix-up augmentation, a technique that generates virtual training samples by interpolating pairs of images and labels, to further enhance the performance and generalization capability of the model. The experimental results showed that ResNet101 and ResNet152 variants outperformed in accurately distinguishing printer sources based on microscopic printed patterns. We developed a mobile app to test the feasibility of our findings in practice. In conclusion, this study aims to lay the groundwork for creating a sufficiently pre-trained model with accurate performance of identification that can be deployed on mobile devices to detect the printed sources of documents.
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References
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., ... & Lerer, A. (2017). Automatic differentiation in pytorch. The 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1125–1134.
Kipphan, H. (2001). Handbook of print media: Technologies and production methods. Springer Science and Business Media.
Nguyen, Q. P., Dang, N. T., Mai, A., & Nguyen, V. S. (2021). Features selection in microscopic printing analysis for source printer identification with machine learning. In International Conference on Future Data and Security Engineering (pp. 210–223). Springer.
Nguyen, Q.-T., Mai, A., Chagas, L., & Reverdy-Bruas, N. (2021). Microscopic printing analysis and application for classification of source printer. Computers & Security, 108, 102320.
Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
Vo, P.-Q., Dang, N. T., Nguyen, Q. P., Mai, A., Nguyen, L. T., Nguyen, Q.-T., & Nguyen, N.-T. (2022). Auto machine learning-based approach for source printer identification. In Recent Challenges in Intelligent Information and Database Systems: 14th Asian Conference, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28-30, 2022, Proceedings (pp. 668–680). Springer.
Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1492–1500.
Zagoruyko, S., & Komodakis, N. (2016). Wide residual networks. arXiv preprint arXiv:1605.07146.
Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017). Mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412.
Zhang, Z., & Sabuncu, M. (2018). Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in Neural Information Processing Systems, 31.