On the Generalization of Deep Learning Models in Video Deepfake Detection.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Deep learning advancements enable sophisticated image and video manipulation, creating deepfakes that challenge authenticity verification.
• Existing deepfake detection systems often fail to generalize to novel manipulation techniques not present in their training data.
• Real-world deepfake detection requires models with robust generalization capabilities.

Purpose of the Study:

• To analyze and compare the generalization capabilities of different deep learning architectures for deepfake detection.
• To identify which deep learning models are most effective at identifying manipulated media across diverse datasets and novel techniques.

Main Methods:

• Comparative analysis of deep learning architectures including Convolutional Neural Networks (CNNs), Vision Transformer, and Swin Transformer.
• Evaluation of model performance based on generalization capabilities across various datasets and manipulation methods.
• Focus on understanding how different architectures learn and represent deepfake anomalies.

Main Results:

• Convolutional Neural Networks (CNNs) demonstrate effectiveness with limited datasets and specific manipulation types.
• Vision Transformers exhibit strong generalization when trained on diverse datasets.
• Swin Transformer shows promise as an attention-based method for limited data scenarios and cross-dataset generalization.

Conclusions:

• Attention-based architectures, particularly the Swin Transformer, provide superior performance for real-world deepfake detection due to enhanced generalization capabilities.
• The choice of architecture significantly impacts deepfake detection efficacy, with Transformers outperforming CNNs in generalizability.
• Future deepfake detection research should prioritize attention-based models for robust real-world applications.