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Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

Asif Rahim1, Yanru Zhong2, Tariq Ahmad3,*, Sadique Ahmad4,*, Mohammed A. ElAffendi4

1 School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
2 Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics, Guilin University of Electronic Technology, Guilin, 541004, China
3 School of Information and Communication, Guilin University of Electronic Technology, Guilin, 541004, China
4 EIAS: Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

* Corresponding Authors: Tariq Ahmad. Email: email; Sadique Ahmad. Email: email

Computers, Materials & Continua 2023, 76(2), 1947-1976. https://doi.org/10.32604/cmc.2023.039340

Abstract

Authorship verification is a crucial task in digital forensic investigations, where it is often necessary to determine whether a specific individual wrote a particular piece of text. Convolutional Neural Networks (CNNs) have shown promise in solving this problem, but their performance highly depends on the choice of hyperparameters. In this paper, we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification. We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms: Adaptive Moment Estimation (ADAM), Stochastic Gradient Descent (SGD), and Root Mean Squared Propagation (RMSPROP). The model is trained and tested on a dataset of text samples collected from various authors, and the performance is evaluated using accuracy, precision, recall, and F1 score. We compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN model. Our results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%. Furthermore, we demonstrate that hyperparameter tuning can help achieve significant performance improvements, even using a relatively simple model architecture like CNNs. Our findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this choice. Overall, this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic investigations. Our findings have important implications for developing accurate and reliable authorship verification systems, which are crucial for various applications in digital forensics, such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

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APA Style
Rahim, A., Zhong, Y., Ahmad, T., Ahmad, S., ElAffendi, M.A. (2023). Hyper-tuned convolutional neural networks for authorship verification in digital forensic investigations. Computers, Materials & Continua, 76(2), 1947-1976. https://doi.org/10.32604/cmc.2023.039340
Vancouver Style
Rahim A, Zhong Y, Ahmad T, Ahmad S, ElAffendi MA. Hyper-tuned convolutional neural networks for authorship verification in digital forensic investigations. Comput Mater Contin. 2023;76(2):1947-1976 https://doi.org/10.32604/cmc.2023.039340
IEEE Style
A. Rahim, Y. Zhong, T. Ahmad, S. Ahmad, and M.A. ElAffendi "Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations," Comput. Mater. Contin., vol. 76, no. 2, pp. 1947-1976. 2023. https://doi.org/10.32604/cmc.2023.039340



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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