# Leveraging Deep Learning Approaches for Deepfake Detection: A Review

arXiv: https://arxiv.org/abs/2304.01908 · HTML: https://mlsanity.com/paper/2304.01908/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection](https://mlsanity.com/paper/2104.09770/) (2021)
- [DeepFake Detection Based on the Discrepancy Between the Face and its Context](https://mlsanity.com/paper/2008.12262/) (2020)
- [On Improving Cross-dataset Generalization of Deepfake Detectors](https://mlsanity.com/paper/2204.04285/) (2022)
- [Deepfake Detection using Spatiotemporal Convolutional Networks](https://mlsanity.com/paper/2006.14749/) (2020)
- [An Experimental Evaluation on Deepfake Detection using Deep Face Recognition](https://mlsanity.com/paper/2110.01640/) (2021)
- [Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning](https://mlsanity.com/paper/2201.08564/) (2022)
- [Machine Learning Algorithms In User Authentication Schemes](https://mlsanity.com/paper/2110.07826/) (2021)
- [Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication](https://mlsanity.com/paper/2205.13646/) (2022)
- [Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning](https://mlsanity.com/paper/2201.08565/) (2022)
- [A Close Look into Human Activity Recognition Models using Deep Learning](https://mlsanity.com/paper/2204.13589/) (2022)
- [A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features](https://mlsanity.com/paper/2208.00788/) (2022)
- [Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning](https://mlsanity.com/paper/2205.08371/) (2022)
- [Using Deep Learning to Detecting Deepfakes](https://mlsanity.com/paper/2207.13644/) (2022)
- [Continuous User Authentication Using Machine Learning and Multi-Finger Mobile Touch Dynamics with a Novel Dataset](https://mlsanity.com/paper/2207.13648/) (2022)
- [Deep Insights of Deepfake Technology : A Review](https://mlsanity.com/paper/2105.00192/) (2021)

## Built on by (most cited first, 2 of 2)

- [Unmasking Deepfakes: Masked Autoencoding Spatiotemporal Transformers for Enhanced Video Forgery Detection](https://mlsanity.com/paper/2306.06881/) (2023)
- [Recent Advancements In The Field Of Deepfake Detection](https://mlsanity.com/paper/2308.05563/) (2023)

## Abstract (arXiv)

Conspicuous progression in the field of machine learning and deep learning have led the jump of highly realistic fake media, these media oftentimes referred as deepfakes. Deepfakes are fabricated media which are generated by sophisticated AI that are at times very difficult to set apart from the real media. So far, this media can be uploaded to the various social media platforms, hence advertising it to the world got easy, calling for an efficacious countermeasure. Thus, one of the optimistic counter steps against deepfake would be deepfake detection. To undertake this threat, researchers in the past have created models to detect deepfakes based on ML/DL techniques like Convolutional Neural Networks. This paper aims to explore different methodologies with an intention to achieve a cost-effective model with a higher accuracy with different types of the datasets, which is to address the generalizability of the dataset.