# Recent Advancements In The Field Of Deepfake Detection

arXiv: https://arxiv.org/abs/2308.05563 · HTML: https://mlsanity.com/paper/2308.05563/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 35 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 26 of 26)

- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [ID-Reveal: Identity-aware DeepFake Video Detection](https://mlsanity.com/paper/2012.02512/) (2020)
- [DeepFake Detection by Analyzing Convolutional Traces](https://mlsanity.com/paper/2004.10448/) (2020)
- [Deepfake Video Detection Using Convolutional Vision Transformer](https://mlsanity.com/paper/2102.11126/) (2021)
- [Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds](https://mlsanity.com/paper/2105.06496/) (2021)
- [An Examination of Fairness of AI Models for Deepfake Detection](https://mlsanity.com/paper/2105.00558/) (2021)
- [GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection](https://mlsanity.com/paper/2207.10246/) (2022)
- [Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation](https://mlsanity.com/paper/2102.09603/) (2021)
- [Identity-Driven DeepFake Detection](https://mlsanity.com/paper/2012.03930/) (2020)
- [Human Perception of Audio Deepfakes](https://mlsanity.com/paper/2107.09667/) (2021)
- [Deepfake detection: humans vs. machines](https://mlsanity.com/paper/2009.03155/) (2020)
- [Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication](https://mlsanity.com/paper/2205.13646/) (2022)
- [SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection](https://mlsanity.com/paper/2210.06105/) (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)
- [DeePhy: On Deepfake Phylogeny](https://mlsanity.com/paper/2209.09111/) (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)
- [Aggregating Layers for Deepfake Detection](https://mlsanity.com/paper/2210.05478/) (2022)
- [Exploring Spatial-Temporal Features for Deepfake Detection and Localization](https://mlsanity.com/paper/2210.15872/) (2022)
- [Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM](https://mlsanity.com/paper/2302.10280/) (2023)
- [Practical Deepfake Detection: Vulnerabilities in Global Contexts](https://mlsanity.com/paper/2206.09842/) (2022)
- [Leveraging Deep Learning Approaches for Deepfake Detection: A Review](https://mlsanity.com/paper/2304.01908/) (2023)
- [Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection](https://mlsanity.com/paper/2210.00361/) (2022)
- [Hybrid Deepfake Detection Utilizing MLP and LSTM](https://mlsanity.com/paper/2304.14504/) (2023)

## Abstract (arXiv)

A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced with an image of someone else. Deepfakes have the potential to cause a variety of problems and are often used maliciously. A common usage is altering videos of prominent political figures and celebrities. These deepfakes can portray them making offensive, problematic, and/or untrue statements. Current deepfakes can be very realistic, and when used in this way, can spread panic and even influence elections and political opinions. There are many deepfake detection strategies currently in use but finding the most comprehensive and universal method is critical. So, in this survey we will address the problems of malicious deepfake creation and the lack of universal deepfake detection methods. Our objective is to survey and analyze a variety of current methods and advances in the field of deepfake detection.