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

Decoding Deception: Error Level Analysis for Image Forgery Detection

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