# Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM

arXiv: https://arxiv.org/abs/2302.10280 · HTML: https://mlsanity.com/paper/2302.10280/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://mlsanity.com/paper/1812.08685/) (2018)
- [The Deepfake Detection Challenge (DFDC) Preview Dataset](https://mlsanity.com/paper/1910.08854/) (2019)
- [DeepFake Detection by Analyzing Convolutional Traces](https://mlsanity.com/paper/2004.10448/) (2020)
- [Unmasking DeepFakes with simple Features](https://mlsanity.com/paper/1911.00686/) (2019)
- [Fighting deepfakes by detecting GAN DCT anomalies](https://mlsanity.com/paper/2101.09781/) (2021)
- [Wavelet-Packets for Deepfake Image Analysis and Detection](https://mlsanity.com/paper/2106.09369/) (2021)
- [Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection](https://mlsanity.com/paper/2109.05701/) (2021)
- [On the Exploitation of Deepfake Model Recognition](https://mlsanity.com/paper/2204.04513/) (2022)
- [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)
- [A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods](https://mlsanity.com/paper/2110.07832/) (2021)
- [Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication](https://mlsanity.com/paper/2205.13646/) (2022)
- [Adversarially robust deepfake media detection using fused convolutional neural network predictions](https://mlsanity.com/paper/2102.05950/) (2021)

## Built on by (most cited first, 3 of 3)

- [Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion](https://mlsanity.com/paper/2305.16353/) (2023)
- [Recent Advancements In The Field Of Deepfake Detection](https://mlsanity.com/paper/2308.05563/) (2023)
- [Enhancing Deepfake Detection using SE Block Attention with CNN](https://mlsanity.com/paper/2506.10683/) (2025)

## Abstract (arXiv)

Social media is currently being used by many individuals online as a major source of information. However, not all information shared online is true, even photos and videos can be doctored. Deepfakes have recently risen with the rise of technological advancement and have allowed nefarious online users to replace one face with a computer generated face of anyone they would like, including important political and cultural figures. Deepfakes are now a tool to be able to spread mass misinformation. There is now an immense need to create models that are able to detect deepfakes and keep them from being spread as seemingly real images or videos. In this paper, we propose a new deepfake detection schema using two popular machine learning algorithms.