Jurnal Teknik Informatika (JUTIF)
Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022

IDENTIFICATION OF BIOMETRIC DEEPFAKES USING FEATURE LEARNING DEEP LEARNING

Anita Sindar Sinaga (Department Information Technology, STMIK Pelita Nusantara Medan, Indonesia)
Arjon Samuel Sitio (Department Digital Business, STMIK Pelita Nusantara Medan, Indonesia)
Sumitra Dewi (Department Information Technology, STMIK Pelita Nusantara Medan, Indonesia)



Article Info

Publish Date
22 Jul 2022

Abstract

Improved image quality on several frames extracted from video by manipulating image parameters by improving object edges and coloring segmentation to identify individual human biometric parts. Convolutional Neural Network (CNN) is designed to process two-dimensional data on images by classifying labeled data using the supervised learning method. The classification of fake or not fake images is done using the feature learning Deep Learning technique by forming a Machine Learning model. Video samples (testing and testing) are taken from YouTube randomly. Identifying the resemblance of one person's face to another's (real) face using deep learning. Identifying the resemblance of a person's face to another's face (real) on a genuine or fake label using CNN. Overall, the accuracy results models obtained the highest average accuracy on the face = 93.40%, mouth = 88.52%, eyes = 89.75 %. average accuracy = 90%.

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Journal Info

Abbrev

jurnal

Publisher

Subject

Computer Science & IT

Description

Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, ...