Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity Detection
This addresses the challenge of verifying digital media authenticity against increasingly convincing deepfakes, though it appears incremental as it applies existing methods to a known problem.
The paper tackled the problem of detecting deepfake videos using deep learning, specifically with MTCNN for face detection and EfficientNet-B5 as an encoder, achieving 93.80% AUC and 86.82% F1 score on the DFDC dataset.
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.