# Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity Detection

arXiv: https://arxiv.org/abs/2505.06528 · HTML: https://mlsanity.com/paper/2505.06528/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 10 May 2025.

## Builds on (most cited first, 8 of 8)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Combining EfficientNet and Vision Transformers for Video Deepfake Detection](https://mlsanity.com/paper/2107.02612/) (2021)
- [Unmasking DeepFakes with simple Features](https://mlsanity.com/paper/1911.00686/) (2019)
- [Detecting Deep-Fake Videos from Appearance and Behavior](https://mlsanity.com/paper/2004.14491/) (2020)
- [Video Face Manipulation Detection Through Ensemble of CNNs](https://mlsanity.com/paper/2004.07676/) (2020)
- [Unmasking Deepfakes: Masked Autoencoding Spatiotemporal Transformers for Enhanced Video Forgery Detection](https://mlsanity.com/paper/2306.06881/) (2023)
- [Minor Privacy Protection Through Real-time Video Processing at the Edge](https://mlsanity.com/paper/2005.01178/) (2020)
- [Classification and understanding of cloud structures via satellite images with EfficientUNet](https://mlsanity.com/paper/2009.12931/) (2020)

## Abstract (arXiv)

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.