Spoofing speech attack detection based on EfficientNet
Zhuoyi Su · 2024
With the rapid development and wide application of speech technology, the use of spoofing speech has become a serious problem. Spoofing speech can be maliciously used to fake information, identity fraud and other activities, which brings serious risks to society. Therefore, effective spoofing speech attack detection methods become essential. In this paper, we propose a spoofing speech attack detection method based on EfficientNet. The feature maps obtained by speech conversion are input into the designed EfficientNet, and a classifier is used to determine the spoofing speech. The experimental results show that the EficientNets-B5 detection model is more than 90% accurate in dealing with various types of spoofing attacks, and the equal error rate is the lowest of 5.82%.