Manuel Milling

Defending against deepfake speech attacks via feature dropout and dropin regularization in large audio language models

Deepfakes have raised widespread concern owing to their threats to privacy, security, and societal trust, driving growing research interest in effective detection methods. Audio deepfake detection has moved from handcrafted-feature classifiers to end-to-end deep learning architectures and, more recently, to self-superv...