GenDF: A Simple Framework for Generalized Deepfake Detection
Analysis
This paper addresses the critical and timely problem of deepfake detection, which is becoming increasingly important due to the advancements in generative AI. The proposed GenDF framework offers a novel approach by leveraging a large-scale vision model and incorporating specific strategies to improve generalization across different deepfake types and domains. The emphasis on a compact network design with few trainable parameters is also a significant advantage, making the model more efficient and potentially easier to deploy. The paper's focus on addressing the limitations of existing methods in cross-domain settings is particularly relevant.
Key Takeaways
- •Proposes GenDF, a novel framework for deepfake detection.
- •Leverages a large-scale vision model for feature extraction.
- •Employs deepfake-specific representation learning and feature space redistribution.
- •Achieves state-of-the-art generalization performance with a compact model (0.28M parameters).
- •Addresses the limitations of existing methods in cross-domain and cross-manipulation settings.
“GenDF achieves state-of-the-art generalization performance in cross-domain and cross-manipulation settings while requiring only 0.28M trainable parameters.”