Image Manipulation Detection Using Error Level Analysis and Deep Learning

Authors

  • Prajakta Kubal Student, Department of Computer Engineering, Ramrao Adik Institute of Technology, Dr.D.Y.Patil Deemed to be University, Nerul, Navi Mumbai, India
  • Vanita Mane Faculty, Department of Computer Engineering, Ramrao Adik Institute of Technology, Dr.D.Y.Patil Deemed to be University, Nerul, Navi Mumbai, India
  • Namita Pulgam Faculty, Department of Computer Engineering, Ramrao Adik Institute of Technology, Dr.D.Y.Patil Deemed to be University, Nerul, Navi Mumbai, India

Keywords:

Convolution neural network, Deep learning, Error Level Analysis, Image forgery

Abstract

With the increasing prevalence of image forgery facilitated by digital editing software, the need for image verification has become paramount in maintaining image integrity and preventing misuse. In this paper we introduce our implemented system called EACN (Error Analysis and Convolutional Neural Network), which combines error level analysis and CNNs. By evaluating the error rate resulting from image quality reduction, we can determine the authenticity of an image. While metadata analysis has been used for image verification, it is susceptible to manipulation. Our implemented system, EACN (Error Analysis and Convolutional Neural Network), combines error level analysis and Convolutional Neural Networks (CNNs) to analyse error rates in genuine and manipulated images. With an impressive accuracy rate of 92.10%, our system leverages deep learning to provide a robust solution for detecting and identifying forged images, ensuring image integrity, preventing misuse, and safeguarding digital content authenticity.

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Published

21.09.2023

How to Cite

Kubal, P. ., Mane, V. ., & Pulgam, N. . (2023). Image Manipulation Detection Using Error Level Analysis and Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 91–99. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3457

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

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