Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity Detection

TL;DR

This paper presents a deep learning-based method for detecting deepfake videos using convolutional neural networks, achieving high accuracy on a Kaggle dataset to address the challenge of digital media manipulation.

Contribution

It introduces a novel deepfake detection approach combining MTCNN and EfficientNet-B5, demonstrating effective performance on a large benchmark dataset.

Findings

Achieved 86.82% F1 score on Kaggle DFDC dataset

Attained 93.80% AUC in deepfake detection

Model effectively identifies subtle inconsistencies in videos

Abstract

Deepfake videos, produced through advanced artificial intelligence methods now a days, pose a new challenge to the truthfulness of the digital media. As Deepfake becomes more convincing day by day, detecting them requires advanced methods capable of identifying subtle inconsistencies. The primary motivation of this paper is to recognize deepfake videos using deep learning techniques, specifically by using convolutional neural networks. Deep learning excels in pattern recognition, hence, makes it an ideal approach for detecting the intricate manipulations in deepfakes. In this paper, we consider using MTCNN as a face detector and EfficientNet-B5 as encoder model to predict if a video is deepfake or not. We utilize training and evaluation dataset from Kaggle DFDC. The results shows that our deepfake detection model acquired 42.78% log loss, 93.80% AUC and 86.82% F1 score on kaggle's DFDC dataset.