In today's rapidly advancing world of artificial intelligence, voice synthesis and transformation technologies are evolving at an extraordinary pace, providing us with incredibly realistic and natural audio experiences. However, these technological advancements also bring potential security risks, particularly as 'voice cloning' technology could be exploited by malicious individuals, threatening personal privacy and social stability. In response to this challenge, the Intelligent System Security Laboratory of Zhejiang University and Tsinghua University have joined forces to launch a revolutionary audio forgery detection framework - SafeEar. This framework not only efficiently detects forged audio but also ensures privacy protection during the detection process.

In the context of rapid advancements in voice synthesis technology, voice forgery has become increasingly severe, posing significant threats to user privacy and social security. Recently, the Intelligent System Security Laboratory at Zhejiang University and Tsinghua University jointly released a novel voice forgery detection framework called 'SafeEar.' This framework aims to efficiently detect forgeries while protecting the privacy of voice content. The concept behind SafeEar is to cleverly design a decoupled model based on neural audio codecs that effectively separates the acoustic features of voice.