Decoding Deception: Error Level Analysis for Image Forgery Detection

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Charan G N; Bhagyashri R. Hanji; Hemanth Kumar V; J S Naga Vishnu Sai; Jeevan S

Decoding Deception: Error Level Analysis for Image Forgery Detection

Authors: Charan G N, Bhagyashri R. Hanji, Hemanth Kumar V, J S Naga Vishnu Sai, Jeevan S

Unique Paper ID: 162588
Volume: 10
Issue: 10
PageNo: 438-443

Keywords: Image Forgery Detecting ELA CNN Digital Content Verification Tampered Images Flask framework Image Preprocessing.

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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Cite This Article

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

Decoding Deception: Error Level Analysis for Image Forgery Detection

Available:https://ijirt.org/article?manuscript=162588

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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