AI-Powered Deepfake Detection Using CNN and Vision Transformer Architectures
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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.
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References · 5
- 2019Searching for MobileNetV3cited by 1,984
- 2021CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classificationcited by 806
- 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 515
- 2017An Equivalence of Fully Connected Layer and Convolutional Layercited by 14
- 2014The Treasure beneath Convolutional Layers: Cross-convolutional-layer Pooling for Image Classificationcited by 9
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