Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)

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Development of a Deep Learning-Based Image Authentication System

Authors
Devidas Bhat1, *, Nagendra Pai1, Vasudeva Pai1, R. Balasubramani1
1NMAM Institute of Technology, Nitte (Deemed to Be University), Karkala, Karnataka, India, 574110
*Corresponding author. Email: devidasbhat@nitte.edu.in
Corresponding Author
Devidas Bhat
Available Online 16 June 2025.
DOI
10.2991/978-94-6463-762-5_3How to use a DOI?
Keywords
Image Forgery Detection; Error Level Analysis (ELA); Hybrid-Models; Image Forensics; Digital Image Forensics
Abstract

The broad accessibility of digital image editing tools has been resulted in a significant increase in image forgery, raising serious concerns about the authenticity of visual content. This paper presents a comprehensive comparison of various image forgery detection algorithms, including Convolutional Neural Networks (CNNs)[3], Support Vector Machines (SVMs)[2], and Discrete Wavelet Transforms (DWTs)[1], each assessed for their efficiency in identifying manipulated regions. Additionally, we introduce a novel hybrid approach that combines Error Level Analysis (ELA)[22] and CNNs[11], achieving a remarkable efficiency rate of 95% in detecting tampering techniques such as splicing, copy-move & retouching.Our method is rigorously tested on benchmark datasets, demonstrating its superior performance and reliability. By offering insights into both traditional and advanced detection methods. This research advances the field digital image authentication, providing an effective solution for ensuring the integrity of digital imagery across various domains.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)
Series
Advances in Engineering Research
Publication Date
16 June 2025
ISBN
978-94-6463-762-5
ISSN
2352-5401
DOI
10.2991/978-94-6463-762-5_3How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Devidas Bhat
AU  - Nagendra Pai
AU  - Vasudeva Pai
AU  - R. Balasubramani
PY  - 2025
DA  - 2025/06/16
TI  - Development of a Deep Learning-Based Image Authentication System
BT  - Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)
PB  - Atlantis Press
SP  - 14
EP  - 26
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-762-5_3
DO  - 10.2991/978-94-6463-762-5_3
ID  - Bhat2025
ER  -