Exploring the Effectiveness of Machine Learning Algorithms in Image Forgery Detection

Authors

  • Niyati Patel  Research Scholar, Computer Engineering Department, Ipcowala Institute of Engineering and Technology, Dharmaj, Gujarat, India
  • Dr. Premal J.Patel   Assistant Professor, Computer Engineering Department, DEPSTAR, Charusat, Off. Nadiad-Petlad Highway, Changa, Anand, Gujarat, India

DOI:

https://doi.org/10.32628/CSEIT2390669

Keywords:

Machine Learning, Image Forgery Detection, Digital Forensics, Convolutional Neural Networks, Support Vector Machines, Decision Trees, Algorithm Evaluation

Abstract

This study investigates the efficacy of various machine learning algorithms for detecting image forgery, a prevalent issue in the realm of digital media manipulation. The research focuses on assessing the performance of these algorithms in accurately identifying instances of image tampering, aiming to contribute valuable insights to the field of digital forensics. The evaluation encompasses a diverse set of machine learning techniques, including but not limited to convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees. Through rigorous experimentation and comparative analysis, the research aims to discern the strengths and limitations of each algorithm in the context of image forgery detection. The findings of this study hold significance for enhancing the capabilities of digital forensics tools, thereby aiding in the mitigation of fraudulent activities, and ensuring the integrity of visual content in the digital' domain.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2024-02-29

Issue

Volume 10, Issue 1, January-February-2024

Section

Research Articles

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How to Cite

[1]
Niyati Patel, Dr. Premal J.Patel , " Exploring the Effectiveness of Machine Learning Algorithms in Image Forgery Detection" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 10, Issue 1, pp.45-54, January-February-2024. Available at doi : https://doi.org/10.32628/CSEIT2390669