Komparasi Performa VGG19, ResNet50, DenseNet121 dan MobileNetV2 Dalam Mendeteksi Gambar Deepfake

Angeline Angeline; Harni Kusniyati

doi:10.24114/cess.v9i2.58671

Authors

Angeline Angeline Universitas Mercu Buana
Harni Kusniyati Universitas Mercu Buana

DOI:

https://doi.org/10.24114/cess.v9i2.58671

Keywords:

Convolutional Neural Network, deepfake, klasifikasi gambar, VGG19, ResNet50, DenseNet121, MobileNetV2

Abstract

Deepfake secara pesat menjadi potensi ancaman keamanan siber yang dapat memanipulasi gambar, video, bahkan audio dengan sangat realistis sehingga manusia mengalami kesulitan dalam membedakan apakah sebuah media adalah asli atau merupakan hasil manipulasi kecerdasan buatan. CNN menjadi salah satu metode yang dikembangkan sebagai solusi. Banyaknya varian model CNN membuka potensi untuk pengembangan lebih lanjut. Penulis mengumpulkan dari berbagai sumber 1,000 citra wajah asli dan 1,000 citra wajah deepfake yang kemudian diperluas dengan teknik augmentasi data untuk melatih, memvalidasi, dan menguji empat varian model CNN yaitu VGG19, ResNet50, DenseNet121, dan MobileNetV2, dengan tujuan untuk menentukan varian yang paling efektif sebagai basis model yang dapat dikembangkan menjadi detektor deepfake. Evaluasi dan perbandingan performa dengan teknik confusion matrix menunjukkan bahwa di antara keempat model, ResNet50 memiliki performa terbaik dengan akurasi 91,5%, presisi 90%, dan recall 91,3%.

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