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초록
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%.
키워드
Hidden Markov models; Feature extraction; Distortion; Speech recognition; Mel frequency cepstral coefficient; Data models; Transformers; Acoustic systems; Evasion attack; audio domain; deep speech; speech recognition; silent-hidden-voice attack; DEEP NEURAL-NETWORKS
- 제목
- Audio Adversarial Example Detection Using the Audio Style Transfer Learning Method
- 저자
- Kwon, Hyun; Lee, Kangjun; Ryu, Junyeol; Lee, Jun
- 발행일
- 2025
- 유형
- Article
- 저널명
- IEEE Access
- 권
- 13
- 페이지
- 122464 ~ 122472