# AI-Powered Deepfake Detection Using CNN and Vision Transformer Architectures

arXiv: https://arxiv.org/abs/2601.01281 · HTML: https://mlsanity.com/paper/2601.01281/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 3 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [An Equivalence of Fully Connected Layer and Convolutional Layer](https://mlsanity.com/paper/1712.01252/) (2017)
- [The Treasure beneath Convolutional Layers: Cross-convolutional-layer Pooling for Image Classification](https://mlsanity.com/paper/1411.7466/) (2014)

## Abstract (arXiv)

The increasing use of artificial intelligence generated deepfakes creates major challenges in maintaining digital authenticity. Four AI-based models, consisting of three CNNs and one Vision Transformer, were evaluated using large face image datasets. Data preprocessing and augmentation techniques improved model performance across different scenarios. VFDNET demonstrated superior accuracy with MobileNetV3, showing efficient performance, thereby demonstrating AI's capabilities for dependable deepfake detection.