Proceedings of the International Conference on Information Systems Design And Intelligent Applications (ICISDA 2026)

International Conference on Information Systems Design And Intelligent Applications (ICISDA 2026)

📍Cebu City, Philippines🗓️ 24-25 July 2026
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Detection of Fraudulent Degree Certificates using Histogram Features and AI Techniques

Authors
M. Sarala1, *, B. L. Muralidhara2, R. Suresh3
1Department of Computer Science(PG), Jyoti Nivas College, Bangalore, India
2Department of Computer Science and Applications, Bangalore University, Bangalore, India
3Department of Statistics, Bangalore University, Bangalore, India
*Corresponding author. Email: sarala@jyotinivas.org
Corresponding Author
M. Sarala
Available Online 31 October 2026.
DOI
10.2991/978-94-6239-821-4_8How to use a DOI?
Keywords
classification; degree certificate; fraudulent detection; histogram features
ABSTRACT

Artificial Intelligence (AI) techniques support detecting fake regions in high-dimensional datasets such as images and videos through fake detection methods. The scope of educational credential forgery is required for authenticating it for admission, and recruitment. In this study, we detected forged educational credentials using machine learning (ML) algorithms such as Support Vector Machine (SVM), NaïveBayes, Logistic Regression, K-Nearest Neighbours (KNN), Random Forest, and Decision Tree. We have collected the primary dataset of degree certificates, and extracted the features using the histogram features (HF): skewness, mean, kurtosis, standard deviation, and entropy from preprocessed certificate images, as structured data is essential for ML models. Further, we conducted a detailed empirical evaluation by fine-tuning the hyperparameters of each model to improve classification performance. Among the algorithms tested, Random Forest attained a good accuracy of 99.4%, surpassing the other methods in detecting fraudulent degree certificates.

Copyright
© 2026 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 Information Systems Design And Intelligent Applications (ICISDA 2026)
Series
Advances in Intelligent Systems Research
Publication Date
31 October 2026
ISBN
978-94-6239-821-4
ISSN
1951-6851
DOI
10.2991/978-94-6239-821-4_8How to use a DOI?
Copyright
© 2026 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  - M. Sarala
AU  - B. L. Muralidhara
AU  - R. Suresh
PY  - 2026
DA  - 2026/10/31
TI  - Detection of Fraudulent Degree Certificates using Histogram Features and AI Techniques
BT  - Proceedings of the International Conference on Information Systems Design And Intelligent Applications (ICISDA 2026)
PB  - Atlantis Press
SP  - 76
EP  - 88
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-821-4_8
DO  - 10.2991/978-94-6239-821-4_8
ID  - Sarala2026
ER  -