Earticle

초록

영어

Our study analyzed and evaluated the attack performance of hybrid combinations of Gaussian, Salt & Pepper, and Sine wave-based noise attack models. The experimental results confirmed that the hybrid attack model had the greatest impact on classification accuracy. Although some attacks did not change the output scores, misclassification was observed in emotional label classification. Our study validated that Gaussian, Salt & Pepper, and Sine wave-based noise attacks exploit security vulnerabilities that affect the learning of speech recognition systems and demonstrated the threat posed by hybrid attack models. The Gaussian model showed a significant decrease in emotion scores, while other models maintained high emotion scores. In terms of classification accuracy, changes were observed in the Gaussian, salt and pepper, and hybrid Gaussian models. Emotion attacks were demonstrated in the hybrid Gaussian, Salt & Pepper, and Hybrid sine wave experiments.

목차

Abstract
1. Introduction
2. Related Research
3. GSS based Hybrid Audio Adversarial Attack Model
4. Experiments and Results of GSS-based Hybrid Attack Models
4.1 Adversarial Attack Models of Gauss, Salt and Pepper, Sine wave, and Hybrid Voice Recognition
4.2 Test & Evaluation for attack models for GSS
4. Conclusions
Acknowledgement
References

키워드

저자

• Jin-keun Hong [ Professor, Division of Advanced IT/X-TEC, Baekseok University, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380