Audio Adversarial Example Detection Using the Audio Style Transfer Learning Method

作     者：Kwon, Hyun Lee, Kangjun Ryu, Junyeol Lee, Jun 

作者机构：Department of Artificial Intelligence and Data Science Korea Military Academy Seoul Republic of Korea Department of Software Science and Engineering Kunsan National University South Korea Department of systems & Mechanical Engineering Korea Military Academy Seoul Korea Division of Computer Information and Science Hoseo University Asan-si South Korea 

出 版 物：《IEEE Access》 (IEEE Access)

年 卷 期：2023年

页      面：1-1页

核心收录：

主　　题：Speech recognition 

摘      要：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%. Author