Detecting Deepfake Videos Using CNN and GRU Methods: Evaluating Performance on the Celeb-DF(v2) Dataset

Rusdi Afandi; Bedy Purnama

doi:10.30865/json.v7i2.9372

Authors

Rusdi Afandi Universitas Telkom Bandung
Bedy Purnama Universitas Telkom Bandung

DOI:

https://doi.org/10.30865/json.v7i2.9372

Keywords:

deepfake, CNN, GRU, video detection, deep learning

Abstract

The development of deep learning technology has allowed the emergence of the phenomenon of deepfakes, which is the manipulation of digital videos that resemble real videos with a high level of realism. These technologies pose serious threats to privacy, digital security, and the spread of false information. As the quality of deepfake videos increases, the detection of this fake content becomes increasingly challenging. This study aims to design and evaluate a deepfake video detection model using a combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU). CNN is used to extract spatial features from each video frame, while GRU is used to capture the temporal relationships between frames. The dataset used is Celeb-DF(v2), which is a benchmark dataset that contains real videos and high-quality deepfake videos. The CNN-GRU model was trained and tested on the dataset, and its performance was evaluated using accuracy, precision, recall, and F1-score metrics.

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