Securing Visual Integrity: Machine learning approaches for forged image detection

forged image detection

Authors

  • Rohita Patil Sinhgad College of Engineering, Pune
  • Vrushali Raut Sinhgad College of Engineering, Pune, India
  • S. A. Shirsat Sinhgad College of Engineering, Pune, India
  • Supriya Rajankar Sinhgad College of Engineering, Pune, India
  • Anjali Yadav Sinhgad Institute of Technology and Science Narhe, Pune, India
  • Somnath Wategaonkar Bharati Vidyapeeth College of Engineering, Navi Mumbai, India

DOI:

https://doi.org/10.62110/sciencein.jist.2024.v12.815

Keywords:

Image Forgery, Machine Learning, VGG16, Deep learning

Abstract

Image forgery detection is a critical area of digital forensics, attempting to discover manipulated regions within images to assure their authenticity and integrity. This study investigates the use of machine learning techniques, particularly the Convolutional Neural Networks for image fraud detection. The suggested method involves training classifier to distinguish between original and counterfeit images using extracted features or patches. An image dataset is divided into training and testing sets in this study to facilitate CNN training on patches corresponding to original images. The accuracy of the trained model in identifying phony regions is then evaluated using an additional test set. To measure the effectiveness of the CNN-based forgery detection system, evaluation criteria such as accuracy, precision and recall are used. Proposed system achieves 99.15% accuracy with VGG16 network with tuned parameters.

 

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Author Biographies

  • Rohita Patil, Sinhgad College of Engineering, Pune

    Electronics and Telecommunication Engineering

  • Vrushali Raut, Sinhgad College of Engineering, Pune, India

    Electronics and Telecommunication Engineering

  • S. A. Shirsat, Sinhgad College of Engineering, Pune, India

    Electronics and Telecommunication Engineering

  • Supriya Rajankar, Sinhgad College of Engineering, Pune, India

    Electronics and Telecommunication Engineering

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Published

2024-02-26

Issue

Section

Engineering

URN

How to Cite

Patil, R., Raut, V. ., Shirsat, S. A. ., Rajankar, S. ., Yadav, A. ., & Wategaonkar, S. . (2024). Securing Visual Integrity: Machine learning approaches for forged image detection. Journal of Integrated Science and Technology, 12(5), 815. https://doi.org/10.62110/sciencein.jist.2024.v12.815

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