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TRUSTNET A DEEP LEARNING BASED INTELLIGENT SYSTEM FOR DIGITAL IMAGE AUTHENTICITY AND FORGERY AVOIDANCE

AUTHORS:
K. Vinotha
S. Madhu Priya
A. Selciya
P. Karthikha
S. Ramya
Mentor
Affiliation
Department Of Information TechnologyM.I.E.T Engineering Tiruchirappalli, Tamil Nadu.
DOI: 10.55041/ijsmt.v2i4.094
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

Secure transmission of sensitive information is a critical requirement in modern cyber and defence communication systems. Although conventional encryption techniques provide strong protection, they remain vulnerable when encryption keys are compromised or encrypted data is intercepted. To address this limitation, this paper proposes a Multi-Security Image Cyber Model for highly confidential data transmission. The framework employs a multi-image–based architecture in which five cover images are initially processed, and three are dynamically selected to reduce predictability.

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 04
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Apr 08 2026
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Vinotha, K., Priya, S. M., Selciya, A., Karthikha, P. & Ramya, S. (2026). TrustNet a Deep Learning based intelligent system for digital image authenticity and forgery avoidance. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.094

Vinotha, K., et al.. "TrustNet a Deep Learning based intelligent system for digital image authenticity and forgery avoidance." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.094.

Vinotha, K.,S. Priya,A. Selciya,P. Karthikha, and S. Ramya. "TrustNet a Deep Learning based intelligent system for digital image authenticity and forgery avoidance." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.094.

References
1.Liu, Y., Guan, Q., Zhao, X., & Cao, Y. (2018). Image forgery localization based on multiscale convolutional neural networks. In Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security (pp. 85–90).

2.Wu, Y., Abd-Almageed, W., & Natarajan, P. (2018). Buster net: Detecting copy-move image forgery with source/target localization. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 168–184).

3.Piva, An overview on image forensics, Int. Scholarly Research Notices, 2013. do: https://doi.org/10.1155/2013/496701

4.Wu, J. Zhou, J. Tian, J. Liu, Robust image forgery detection over online social network shared image, in: Proceedings of the IEEE Conference on Comp. Vision and Pattern Recognize., New Orleans, Louisiana, USA, ISBN: 978-1-6654-6946-3, 2022, pp. 13440-

5.13449.

6.Hussain, S. Tan, B. Li, X. Qin, D. Hussain, J. Huang, A novel deep learning framework for double JPEG compression detection of small size blocks, Journal of Visual Commun. and Image Represent. 80(2021) 103269. do: https://doi.org/10.1016/j.jvcir.2021.103269

7.Dong, H. Zeng, Y. Peng, X. Gao, A. Peng, A deep learning approach with data augmentation for median filtering forensics, Multi. Tools and Apply. 81(2022) 11087-105. do:

8.https://doi.org/10.1007/s11042-022-12040-w

9.Marra, D. Gragnani Ello, D. Cozzolino, L. Verdolaga, Detection of gang-generated fake images over social networks, in: Proceedings of the IEEE Conference on Mult. Inf. Process. and Retrieval, Miami, FL, USA, ISBN: 978-1-5386-1857-8, 2018, pp. 384-389

10.Cybersecurity (2025). Cybercrime to cost the world $10.5 trillion annually by. Cybersecurity Ventures Report.

 
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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