USE OF DEEP LEARNING FOR FAKE IMAGE DETECTION

USE OF DEEP LEARNING FOR FAKE IMAGE DETECTION

Registration ID: IJNRD_189682

Published ID: IJNRD2303428

DOI: Click Here to Get

B.V.VAMSI KRISHNA , KAMMARI RYAGNIRVESH , POTHARAJU MAHESH

Convolutional Neural Network (CNN), Local Binary Patterns (LBP), Deep Learning, detection of fake images

Biometric technologies are useful now for identifying people, but criminals alter their look, behaviour and psychological makeup to trick identification systems. We are employing a novel method called Deep Texture Features extraction from photos to solve this issue, followed by the construction of a machine learning model using the CNN (Convolution Neural Networks) algorithm. This method is also known as LBPNet or NLBPNet since it relies so heavily on the LBP (Local Binary Pattern) algorithm for features extraction. In order to identify false face photos, we are proposing an LBP-based machine learning convolution neural network dubbed LBPNET. Here, we will first extract LBP from the photos, and then we will train the convolution neural network on the LBP descriptor images to produce the training model. Every time a new test picture is uploaded, the training model will use that image to determine if the test image contains fraudulent images or not. Listed below are some LBP details.

Published Paper   E-Certificate

"USE OF DEEP LEARNING FOR FAKE IMAGE DETECTION", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 3, page no.e223-e231, March-2023, Available :https://ijnrd.org/papers/IJNRD2303428.pdf

Volume 8 Issue 3, March-2023

Pages : e223-e231

Paper Reg. ID: IJNRD_189682

Published Paper Id: IJNRD2303428

Downloads: 000121157

Research Area: Computer Science & Technology 

Country: HYDERABAD, TELANGANA, INDIA

Published Paper PDF: https://ijnrd.org/papers/IJNRD2303428.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303428

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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