Herd Mentality in Augmentation -- Not a Good Idea! A Robust Multi-stage Approach towards Deepfake Detection
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Abstract (arXiv)
The rapid increase in deepfake technology has raised significant concerns about digital media integrity. Detecting deepfakes is crucial for safeguarding digital media. However, most standard image classifiers fail to distinguish between fake and real faces. Our analysis reveals that this failure is due to the model's inability to explicitly focus on the artefacts typically in deepfakes. We propose an enhanced architecture based on the GenConViT model, which incorporates weighted loss and update augmentation techniques and includes masked eye pretraining. This proposed model improves the F1 score by 1.71% and the accuracy by 4.34% on the Celeb-DF v2 dataset. The source code for our model is available at https://github.com/Monu-Khicher-1/multi-stage-learning
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 18 references that are in the map. First posted on arXiv 7 Oct 2024.
Builds on
15 of 18 references.
References · 15
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- 2019Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Predictioncited by 66
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- 2020DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentationcited by 54
3 more
- 2020SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimizationcited by 26
- 2023GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformercited by 13
- 2020Self-Supervised Physics-Guided Deep Learning Reconstruction For High-Resolution 3D LGE CMRcited by 10
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