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Robust Deepfake Detection via Meta-Learning by feditab627 is a document available to read on EtoBox.

This paper presents an adversarial meta-learning algorithm designed to enhance deepfake detection through a multi-agent framework that addresses challenges such as generalization, robustness, and adaptability to data drift. By integrating task-specific adaptive sample synthesis and a hierarchical retrieval-augmented generation workflow, the proposed model dynamically generates custom deepfake samples for improved detection performance across various datasets. Experimental results demonstrate the model

Author

feditab627

Language

EN