IDENTIFICATION OF BIOMETRIC DEEPFAKES USING FEATURE LEARNING DEEP LEARNING - Anita Sindar Sinaga; Arjon Samuel Sitio; Sumitra Dewi (Informatika, Universitas Jenderal Soedirman, 2022)
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%.