A Software-Driven Document Forgery Detection System Using Deep Learning and Image Forensics

Sahana Kumari B; Thyagaraju G S; Abhishek M; Adarsh; Basavaraj S M; Manoj M H

doi:10.55524/ijirem.2025.12.6.15

Abstract

As digital documents continue to replace physical paperwork, the problem of document forgery has become more frequent and more difficult to catch by the naked eye. Many existing verification methods still rely on manual checking, which is slow and often fails when facing modern editing tools that can alter content without leaving obvious traces. To address this gap, this study proposes a software-driven Document Forgery Detection System that makes use of deep learning, machine-learning techniques, and image-forensic principles. The system examines visual patterns, structural distortions, and inconsistencies in texture and metadata to identify whether a document has been altered. Since the entire system operates through software without requiring any special hardware, it becomes practical even for institutions with limited resources. Experimental evaluation shows that the model can reliably highlight forgery attempts such as copy–paste edits, signature modifications, and tampered text fields. The work demonstrates how AI-based analysis can help organizations verify documents more accurately and reduce fraud in digital workflows.

Keywords

Document Forgery Detection, Metadata Analysis, Text Extraction, Tampering Detection, Machin Learning, Image Processing, Document Authentication

References

Sirajudeen, M., and R. Anitha, “Forgery document detection in information management system using cognitive techniques,” Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8057–8068, 2020. Available from: https://doi.org/10.3233/JIFS-18912
Boonkrong, S., “Design of an academic document forgery detection system,” International Journal of Information Technology, vol. 16, no. 2, pp. 1–13, 2024. Available from: https://link.springer.com/article/10.1007/s41870-024-02006-6
Y. Liu, Q. Guan, and X. Zhao, “Copy-move forgery detection based on convolutional kernel network,” Multimedia Tools and Applications, vol. 77, no. 13, pp. 18269–18293, 2018. Available from: https://link.springer.com/article/10.1007/s11042-017-5374-6
D’Avino, D. Cozzolino, G. Poggi, and L. Verdoliva, “Autoencoder with recurrent neural networks for video forgery detection,” in Media Watermarking, Security, and Forensics 2017, vol. 10399, pp. 92–99, Society for Imaging Science and Technology, 2017. Available from: https://doi.org/10.48550/arXiv.1708.08754
X. Liao, S. Chen, J. Chen, T. Wang, and X. Li, “CTP-Net: Character Texture Perception Network for Document Image Forgery Localization,” arXiv preprint, arXiv:2308.02158, 2023. Available from: https://doi.org/10.48550/arXiv.2308.02158
M. A. A. Al-Ameri, B. Ciylan, and B. Mahmood, “Spectral data analysis for forgery detection in official documents: A network-based approach,” Electronics, vol. 11, no. 23, p. 4036, 2022. Available from: https://doi.org/10.3390/electronics11234036
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, e13020, 2022. Available from: https://doi.org/10.1111/exsy.13020
S. Anandhamurugan, D. Prasanth, R. Sujitha, and N. Mukhilan, “Forgery detection in handwritten documents,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–6, IEEE, 2024. Available from: https://ieeexplore.ieee.org/abstract/document/10725724
M. Hamido, A. Mohialdin, and A. Atia, “The use of background features, template synthesis and deep neural networks in document forgery detection,” in Proceedings of the 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 365–370, 2023. Available from: https://ieeexplore.ieee.org/abstract/document/10067120

Cites this article as

S. K. B, T. G. S, A. M, Adarsh, B. S. M, M. M. H, "A Software-Driven Document Forgery Detection System Using Deep Learning and Image Forensics", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-12, Issue-6, Page No-90-93, 2025. Available from: https://doi.org/10.55524/ijirem.2025.12.6.15

Corresponding Author

Sahana Kumari B

Assistant Professor, Department of Computer Science and Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire, Karnataka, India