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All Journal International Journal of Advances in Applied Sciences
Nishigandha N. Zanje
Symbiosis International (Deemed University)
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Detecting facial image forgeries with transfer learning techniques Nishigandha N. Zanje; Anupkumar M Bongale; Deepak Dharrao
International Journal of Advances in Applied Sciences Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i1.pp93-105

Abstract

Digital images have become ubiquitous in our daily lives, appearing on our smartphone screens and online websites. They are widely used in numerous industries, including media, forensic and criminal investigations, medicine, and more. The ease of access to consumer photo editing tools has made it simple to manipulate images. However, such altered images pose a serious risk in fields where image authenticity is crucial, making it challenging to confirm the reliability of digital images. Digital image fraud involves altering an image's meaning without leaving any obvious signs. In this study, we present three convolutional neural network-based transfer learning techniques “CNN” classification of facial image forgeries, using VGG-19, InceptionV3, and DenseNet201. Among these methods, DenseNet201 achieved the highest accuracy of 99%, followed by InceptionV3 at 94% and VGG-19 at 84%.
Co-Authors Anupkumar M Bongale Deepak Dharrao