Journal of Electronics and Informatics
IRO Journals
Volume 7 — Issue 2 — June 2025

A Comparative Study on Hashing Algorithms for Data Integrity and Efficiency

https://doi.org/10.36548/jei.2025.2.002
Atshaya R.
Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India
Bhavatharni J.
Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India
Darshana S.B.
Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India
Ismankhan Y.M.
Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India
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How to Cite

R., Atshaya, Bhavatharni J., Darshana S.B., and Ismankhan Y.M. 2025. “A Comparative Study on Hashing Algorithms for Data Integrity and Efficiency”. Journal of Electronics and Informatics 7 (2): 95-111. https://doi.org/10.36548/jei.2025.2.002.
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Keywords

— Image forgery detection
— deep learning
— copy-move attack
— splicing attack
— Deep Fake detection
— data integrity
— hashing algorithms
— SHA-256
— CRC32
— Random Projection Hashing
— Count-Min Sketch
— similarity detection
— streaming data analytics
— probabilistic data structures
Published: 20-05-2025

Abstract

In recent years, the widespread availability of image editing tools has led to the proliferation of phony and manipulated photos on the Internet and social media. Various techniques have been developed to detect image forgery and identify altered or fabricated regions, with a growing emphasis on deep learning (DL) methods. This study explores recent advances in DL-based forgery detection algorithms, focusing on the detection of copy-move and splicing attacks two of the most common image tampering techniques. Additionally, the challenges posed by DeepFake-generated content, which often mimics splicing manipulation, are discussed. The study also compares hashing algorithms (SHA-256, CRC32, Random Projection Hashing, and Count-Min Sketch) for use in data integrity, similarity searches, and frequency estimation. Finally, recommendations for selecting suitable algorithms and hybrid approaches are provided to enhance image authentication and large-scale data analysis.

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