Decoding Deception: Error Level Analysis for Image Forgery Detection

  • Unique Paper ID: 162588
  • Volume: 10
  • Issue: 10
  • PageNo: 438-443
  • Abstract:
  • Detecting digital image forgery is crucial to safeguard image integrity, especially in an era where manipulation is effortless. Error Level Analysis serves as a valuable tool by decreasing image quality and comparing error levels to identify modifications. This study employs Convolutional Neural Network, a deep learning method to enhance image forgery detection. By introducing Error Level Analysis extraction, the validation accuracy improves by approximately 2.7%, leading to enhanced test accuracy. However, this enhancement comes at the cost of a 5.6% increase in processing time. The research underscores the trade-offs involved in leveraging ELA within a deep learning framework for more effective image authenticity verification.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{162588,
        author = {Charan G N and Bhagyashri R. Hanji and Hemanth Kumar V and J S Naga Vishnu Sai and Jeevan S},
        title = {Decoding Deception: Error Level Analysis for Image Forgery Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {438-443},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162588},
        abstract = {Detecting digital image forgery is crucial to safeguard image integrity, especially in an era where manipulation is effortless. Error Level Analysis serves as a valuable tool by decreasing image quality and comparing error levels to identify modifications. This study employs Convolutional Neural Network, a deep learning method to enhance image forgery detection. By introducing Error Level Analysis extraction, the validation accuracy improves by approximately 2.7%, leading to enhanced test accuracy. However, this enhancement comes at the cost of a 5.6% increase in processing time. The research underscores the trade-offs involved in leveraging ELA within a deep learning framework for more effective image authenticity verification.},
        keywords = {Image Forgery Detecting, ELA, CNN, Digital Content Verification, Tampered Images, Flask framework, Image Preprocessing.},
        month = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 10
  • PageNo: 438-443

Decoding Deception: Error Level Analysis for Image Forgery Detection

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