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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Sowmya Purushothaman
Nitte Meenakshi Institute of Technology

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Convolutional neural network-based techniques and error level analysis for image tamper detection Vijaya Shetty Sadanand; Shruthi Shetty Janardhana; Sowmya Purushothaman; Sarojadevi Hande; Ramya Prakash
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1100-1107

Abstract

Photographs are the foremost powerful and trustworthy media of expression. At present, digital pictures not only serve forged information but also disseminate deceptive information. Users and experts with various objectives edit digital photographs. Images are frequently used as proof of reality or fact, therefore fake news or any publication that makes use of photos that have been altered in any way has a larger chance of deceiving readers. There is a need for a high-resolution image analysis model that processes individual pixels in images and a substantial amount of diverse image data, to detect image falsification. Convolutional neural network (CNN) with error level analysis (ELA) adopted in this research is found to be an ideal deep learning concept for detecting image manipulation. The model exhibited a validation accuracy of 99.6%, 99.7%, and 99.4% for CASIA V1.0, CASIA V2.0 and MICC datasets respectively. The accuracy for handmade tampered images was found to be 99.2%.
Co-Authors Ramya Prakash Sarojadevi Hande Shruthi Shetty Janardhana Vijaya Shetty Sadanand