A Review on Fake Image Detection Using Machine Learning

Registration ID: IJNRD_191816

Published ID: IJNRD2304413

DOI: Click Here to Get

Sahil Meshram , Vaishali Gedam , Shrinivas chinchanikar , Yash Lad , Ganesh pandey, Punam Bhandarkar

Biometry, Identity, Recognition, Detection, Fake face.

Nowadays biometric systems are useful in recognizing a person’s identity, but criminals change their appearance in behaviour and psychological to deceive recognition system. To overcome this problem we are using a new technique called Deep Texture Features extraction from images and then building train machine learning model using CNN (Convolution Neural Networks) algorithm. This technique refers as LBPNet or NLBPNet as this technique is heavily dependent on features extraction using LBP (Local Binary Pattern) algorithm. In this project, we are designing LBP Based machine learning Convolution Neural Network called LBPNET to detect fake face images. Here first we will extract LBP from images and then train LBP descriptor images with Convolution Neural Network to generate a training model. Whenever we upload a new test image then that test image will be applied to the training model to detect whether the test image contains a fake image or a non-fake image. Below we can see some details on LBP.

Published Paper   E-Certificate

Sahil Meshram, Vaishali Gedam, Shrinivas chinchanikar, Yash Lad, & Ganesh pandey, Punam Bhandarkar (April-2023). A Review on Fake Image Detection Using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), e78-e83. https://ijnrd.org/papers/IJNRD2304413.pdf

Volume 8 Issue 4, April-2023

Pages : e78-e83

Paper Reg. ID: IJNRD_191816

Published Paper Id: IJNRD2304413

Downloads: 000121976

Research Area: Computer Science & Technology 

Country: Nagpur, maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304413.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304413

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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