Deepfake Attribution with Asymmetric Learning for Open-World Detection

This ArXiv paper explores deepfake detection, a crucial area of research given the increasing sophistication of AI-generated content. The application of confidence-aware asymmetric learning represents a novel approach to addressing the challenges of open-world deepfake attribution.

• •Addresses the challenge of detecting deepfakes in an open-world scenario.
• •Employs confidence-aware asymmetric learning, suggesting a novel technical approach.
• •Research paper, potentially contributing to advancements in digital forensics and media integrity.