Audio Anti Spoofing

Audio anti-spoofing (AAS) focuses on developing robust methods to detect fake audio, such as deepfakes and impersonations, safeguarding against malicious use of voice technology. Current research emphasizes improving the generalization of detection models across diverse spoofing techniques and acoustic conditions, employing deep learning architectures like convolutional neural networks and vision transformers, often incorporating techniques like contrastive learning and multi-order spectrogram analysis to enhance performance. The significance of AAS lies in its crucial role in securing voice-based authentication systems and combating the spread of misinformation, with ongoing efforts to improve accuracy, robustness, and interpretability of detection models.