Audio Adversarial Example Detection Using the Audio Style Transfer Learning Method

Hyun Kwon, Kangjun Lee, Junyeol Ryu, Jun Lee

Abstract: In this paper, we propose a method for defending against audio adversarial examples that operates by applying audio style transfer learning. The proposed method has the effect of maintaining the classification result produced by the target model and removing the adversarial noise by changing only the style while maintaining the content of the input audio sample. In an experimental evaluation using the Mozilla Common Voice dataset as the test data source and TensorFlow as the machine learning library, the proposed method improved the target model’s accuracy on the adversarial examples from 2.1% to 79.2% while maintaining its accuracy on the original samples at 81.4%.

External IDs:dblp:journals/access/KwonLRL25