Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition

Wenhan Yao, Jiangkun Yang, Yongqiang He, Jia Liu, Weiping Wen·June 16, 2024

Summary

The paper investigates Imperceptible Rhythm Backdoor Attacks in speech recognition systems, focusing on Random Spectrogram Rhythm Transformation (RSRT), a non-neural method that subtly alters mel spectrograms' rhythm without affecting speech quality or content. RSRT is used to embed stealthy triggers, achieving high attack success rates even with minimal poisoning. The study demonstrates RSRT's effectiveness through speaker verification, keyword spotting, and emotion recognition tasks, showing its potential for undetectable backdoor attacks. Key points include the use of VAD, neural vocoder, and the ability to maintain naturalness while targeting rhythm as a trigger. The research contributes to the understanding of physical backdoor threats in speech recognition, emphasizing the importance of stealth and security in these systems.

Key findings

2

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of backdoor attacks in speech recognition systems by proposing a method called Random Spectrogram Rhythm Transformation (RSRT) to generate stealthy poisoned utterances with transformed rhythms . This problem is not entirely new as backdoor attacks have been researched in speech recognition systems in recent years, typically based on data poisoning methods . The proposed method focuses on modifying the rhythm component of speech to create poisoned samples that are difficult to detect by human hearing or automatic algorithms, thus enhancing the stealthiness of the attack .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to exploring rhythm transformation for embedding undetectable vulnerabilities on speech recognition . The research focuses on developing a method for speech backdoor attacks that involves transforming the rhythm or speech components of speech to embed vulnerabilities that are difficult to detect by speaker verification systems and automatic speech recognition . The proposed approach involves a non-neural algorithm called Random Spectrogram Rhythm Transformation (RSRT) to generate stealthy poisoned utterances by modifying the spectrograms of speech and then reconstructing them using a neural network vocoder . The study seeks to demonstrate the effectiveness and stealthiness of this rhythm trigger in backdoor attacks on speech recognition systems, emphasizing the importance of maintaining the naturalness and intelligibility of the converted speech while achieving a high attack success rate with a low poisoning rate .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel speech backdoor attack method that combines Voice Activity Detection (VAD), RSRT, and neural vocoder techniques to achieve high Automatic Speech Recognition (ASR) performance with a low poisoning rate . The method introduces a trigger that can evade detection by speaker verification systems and ASR, ensuring excellent stealthiness and speech quality . By altering the rhythm or speech components of speech, the paper explores a new approach to speech backdoor attacks, demonstrating efficient and stealthy performance . Additionally, the ablation study in the paper evaluates the ASR performance with different hyper-parameters and poisoned numbers, showcasing the effectiveness of squeezing over stretching in achieving high ASR scores . The experiments in the paper highlight the method's success in achieving high ASR accuracy while maintaining a low poisoning rate, emphasizing the importance of changing speech rhythm and components in backdoor attacks . The proposed speech backdoor attack method in the paper introduces a unique trigger that can evade detection by speaker verification systems and automatic speech recognition, ensuring excellent stealthiness and speech quality . By utilizing Random Spectrogram Rhythm Transformation (RSRT) combined with neural vocoder techniques, the method achieves high Automatic Speech Recognition (ASR) performance with a low poisoning rate . This approach involves altering the rhythm and speech components of speech, providing a new exploratory method for speech backdoor attacks that demonstrates efficiency and stealthiness . The ablation study conducted in the paper evaluates ASR performance with different hyper-parameters and poisoned numbers, highlighting the effectiveness of squeezing over stretching in achieving high ASR scores .

Compared to previous methods, the proposed method stands out due to its ability to maintain very high ASR performance while keeping a low poisoning rate, showcasing excellent stealthiness and efficiency in speech backdoor attacks . The method's trigger can successfully avoid detection by speaker verification systems and ASR, emphasizing the importance of changing speech rhythm and components in achieving effective backdoor attacks . Additionally, the experiments conducted in the paper demonstrate the method's success in achieving high ASR accuracy with minimal poisoning, underlining the significance of rhythm transformation in enhancing attack success rates .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of backdoor attacks on deep learning systems, particularly in the context of speech recognition. Noteworthy researchers in this field include J. Zhang, A. Fu, S. Nepal, H. Kim, T. Gu, K. Liu, B. Dolan-Gavitt, S. Garg, A. Turner, D. Tsipras, A. Madry, J. Dai, C. Chen, Y. Li, X. Pan, M. Zhang, B. Sheng, J. Zhu, M. Yang, T. Zhai, Y. Li, Z. Zhang, B. Wu, Y. Jiang, S.-T. Xia, C. Shi, T. Zhang, Z. Li, H. Phan, T. Zhao, Y. Wang, J. Liu, B. Yuan, Y. Chen, among others .

The key solution mentioned in the paper "Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition" involves a method called Random Spectrogram Rhythm Transformation (RSRT). This non-neural and fast algorithm aims to generate stealthy poisoned utterances by stretching or squeezing the mel spectrograms and then recovering them back to signals. This approach maintains the timbre and content of the speech unchanged, enhancing the stealthiness of the backdoor attack. The experiments conducted with this method demonstrate excellent effectiveness and stealthiness in speech recognition tasks .


How were the experiments in the paper designed?

The experiments in the paper were designed with a specific methodology:

  • The experiments focused on two types of speech recognition tasks: keyword spotting (KWS) and text-independent speech emotion recognition (TSER) .
  • For the KWS task, the Google Speech Commands Dataset v2 was used, containing over 65,000 audios of 35 categories of keywords. The dataset was divided into training, validation, and test sets, with poisoned samples only present in the training set. Four KWS networks were utilized for the experiments .
  • The TSER task utilized the Emotional Speech Dataset (ESD) for emotional speech recognition. The dataset consisted of utterances spoken by native English and Chinese speakers across 5 emotion categories. Signal process deep neural models were employed for the TSER experiments .
  • The experiments aimed to test the stealthiness of poisoned samples through speaker verification and automatic speech recognition. The results demonstrated the effectiveness and stealthiness of the proposed method .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the Google Speech Commands Dataset v2, which contains more than 65,000 audios of 35 categories of keywords for keyword-spotting devices . The code for the proposed attack pipeline is open source, as the study mentions using the AST system for training victim models and paraformer for evaluation, both of which have open-source implementations .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper proposed a speech backdoor attack method that combines various techniques like VAD, RSRT, and neural vocoder to achieve high ASR with a low poisoning rate . The experiments demonstrated the excellent performance in terms of efficiency and stealthiness of speech backdoor attacks using their method . The proposed trigger was effective in avoiding detection by speaker verification systems and automatic speech recognition, showcasing excellent stealthiness and speech quality .

The experiments conducted in the paper included evaluation metrics on poisoned samples to verify the consistency of speech components. The results showed that the rhythm trigger achieved a high attack success rate with a very low poisoning rate, confirming the effectiveness of the proposed method . The paper's contributions were summarized, highlighting the design of a non-neural rhythm transformation poisoning pipeline that demonstrated good stealthiness and effectiveness in backdoor attacks on speech recognition systems .

Furthermore, the paper conducted three types of evaluation experiments to demonstrate the stealthiness of the proposed trigger. These evaluations focused on detecting timbre consistency by SVS and content consistency by ASR, proving that the poisoned samples were challenging for defenders to detect, thus possessing good stealthiness . The results of the attack effectiveness and stealthiness evaluation supported the scientific hypotheses put forward in the paper, showcasing the success of the proposed method in achieving high attack success rates with minimal poisoning rates .


What are the contributions of this paper?

The contributions of the paper "Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition" include:

  • Designing a non-neural rhythm transformation poisoning pipeline with Random Spectrogram Rhythm Transformation (RSRT) to modify the spectrograms of utterances through stretching or squeezing operations, ensuring naturalness and intelligibility when reconstructed into speech using a neural network vocoder .
  • Conducting backdoor attack experiments on Keyword Spotting (KWS) and Text-Independent Speech Emotion Recognition (TSER) tasks, demonstrating the effectiveness and stealthiness of the proposed trigger .
  • Performing three types of evaluation experiments to validate the stealthiness of the trigger, including detecting timbre consistency by Speaker Verification System (SVS) and content consistency by Automatic Speech Recognition (ASR), proving the difficulty for defenders to identify the poisoned samples and their high stealthiness .

What work can be continued in depth?

Further research in the field of imperceptible rhythm backdoor attacks on speech recognition systems can be expanded in several directions:

  • Exploration of Non-Neural Algorithms: Research can delve deeper into the development and enhancement of non-neural algorithms like Random Spectrogram Rhythm Transformation (RSRT) to improve the stealthiness of data poisoning in speech recognition systems .
  • Enhancing Attack Success Rate: There is potential for further investigation into methods that can increase the attack success rate while maintaining a low poisoning rate, as demonstrated by the proposed rhythm trigger in the research .
  • Investigation of New Trigger Mechanisms: Future studies could focus on exploring and developing new trigger mechanisms that can effectively embed undetectable vulnerabilities in speech recognition systems, ensuring both effectiveness and stealthiness .
  • Evaluation of Different Speech Recognition Tasks: Research can be extended to evaluate the effectiveness and stealthiness of poisoned samples in various speech recognition tasks, such as speaker verification and automatic speech recognition, to gain a comprehensive understanding of the impact of backdoor attacks .
  • Comparison with Existing Backdoor Methods: Further analysis and comparison with existing backdoor methods based on data poisoning can provide insights into the strengths and weaknesses of different approaches, aiding in the development of more robust defense mechanisms against such attacks .

Tables

2

Introduction
Background
Non-neural method: RSRT as a novel approach
Importance of stealth in backdoor attacks
Objective
Investigate RSRT effectiveness
Assess attack success rates and undetectability
Highlight potential threats to physical security in speech systems
Methodology
Data Collection
Random Spectrogram Rhythm Transformation (RSRT) implementation
Poisoning dataset creation
Target tasks: speaker verification, keyword spotting, emotion recognition
Data Preprocessing
Voice Activity Detection (VAD)
Use of VAD to isolate speech segments
Impact on rhythm manipulation
Neural Vocoder
Integration of neural vocoder for maintaining naturalness
Role in preserving speech quality
Attack Embedding
Stealthy trigger insertion through RSRT
Minimal poisoning requirement for high success rates
Evaluation
Performance analysis on target tasks
Comparison with existing defense mechanisms
Detection and robustness tests
Results and Discussion
Attack success rates across different tasks
Naturalness and speech quality preservation
Implications for physical security in speech recognition systems
Security Implications
Physical backdoor threats in the context of speech recognition
Need for secure design and robustness testing
Countermeasures and future research directions
Conclusion
Summary of findings and contributions
Limitations and future work
Importance of addressing rhythm-based backdoors in speech systems
References
Cited works on rhythm manipulation, backdoor attacks, and speech recognition security
Basic info
papers
sound
audio and speech processing
artificial intelligence
Advanced features
Insights
How does RSRT differ from neural methods in altering mel spectrograms?
What are the key factors that make RSRT a stealthy backdoor technique?
What are the tasks the study employs to demonstrate RSRT's effectiveness?
What type of attack is the paper primarily concerned with in speech recognition systems?

Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition

Wenhan Yao, Jiangkun Yang, Yongqiang He, Jia Liu, Weiping Wen·June 16, 2024

Summary

The paper investigates Imperceptible Rhythm Backdoor Attacks in speech recognition systems, focusing on Random Spectrogram Rhythm Transformation (RSRT), a non-neural method that subtly alters mel spectrograms' rhythm without affecting speech quality or content. RSRT is used to embed stealthy triggers, achieving high attack success rates even with minimal poisoning. The study demonstrates RSRT's effectiveness through speaker verification, keyword spotting, and emotion recognition tasks, showing its potential for undetectable backdoor attacks. Key points include the use of VAD, neural vocoder, and the ability to maintain naturalness while targeting rhythm as a trigger. The research contributes to the understanding of physical backdoor threats in speech recognition, emphasizing the importance of stealth and security in these systems.
Mind map
Role in preserving speech quality
Integration of neural vocoder for maintaining naturalness
Impact on rhythm manipulation
Use of VAD to isolate speech segments
Detection and robustness tests
Comparison with existing defense mechanisms
Performance analysis on target tasks
Minimal poisoning requirement for high success rates
Stealthy trigger insertion through RSRT
Neural Vocoder
Voice Activity Detection (VAD)
Target tasks: speaker verification, keyword spotting, emotion recognition
Poisoning dataset creation
Random Spectrogram Rhythm Transformation (RSRT) implementation
Highlight potential threats to physical security in speech systems
Assess attack success rates and undetectability
Investigate RSRT effectiveness
Importance of stealth in backdoor attacks
Non-neural method: RSRT as a novel approach
Cited works on rhythm manipulation, backdoor attacks, and speech recognition security
Importance of addressing rhythm-based backdoors in speech systems
Limitations and future work
Summary of findings and contributions
Countermeasures and future research directions
Need for secure design and robustness testing
Physical backdoor threats in the context of speech recognition
Implications for physical security in speech recognition systems
Naturalness and speech quality preservation
Attack success rates across different tasks
Evaluation
Attack Embedding
Data Preprocessing
Data Collection
Objective
Background
References
Conclusion
Security Implications
Results and Discussion
Methodology
Introduction
Outline
Introduction
Background
Non-neural method: RSRT as a novel approach
Importance of stealth in backdoor attacks
Objective
Investigate RSRT effectiveness
Assess attack success rates and undetectability
Highlight potential threats to physical security in speech systems
Methodology
Data Collection
Random Spectrogram Rhythm Transformation (RSRT) implementation
Poisoning dataset creation
Target tasks: speaker verification, keyword spotting, emotion recognition
Data Preprocessing
Voice Activity Detection (VAD)
Use of VAD to isolate speech segments
Impact on rhythm manipulation
Neural Vocoder
Integration of neural vocoder for maintaining naturalness
Role in preserving speech quality
Attack Embedding
Stealthy trigger insertion through RSRT
Minimal poisoning requirement for high success rates
Evaluation
Performance analysis on target tasks
Comparison with existing defense mechanisms
Detection and robustness tests
Results and Discussion
Attack success rates across different tasks
Naturalness and speech quality preservation
Implications for physical security in speech recognition systems
Security Implications
Physical backdoor threats in the context of speech recognition
Need for secure design and robustness testing
Countermeasures and future research directions
Conclusion
Summary of findings and contributions
Limitations and future work
Importance of addressing rhythm-based backdoors in speech systems
References
Cited works on rhythm manipulation, backdoor attacks, and speech recognition security
Key findings
2

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of backdoor attacks in speech recognition systems by proposing a method called Random Spectrogram Rhythm Transformation (RSRT) to generate stealthy poisoned utterances with transformed rhythms . This problem is not entirely new as backdoor attacks have been researched in speech recognition systems in recent years, typically based on data poisoning methods . The proposed method focuses on modifying the rhythm component of speech to create poisoned samples that are difficult to detect by human hearing or automatic algorithms, thus enhancing the stealthiness of the attack .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to exploring rhythm transformation for embedding undetectable vulnerabilities on speech recognition . The research focuses on developing a method for speech backdoor attacks that involves transforming the rhythm or speech components of speech to embed vulnerabilities that are difficult to detect by speaker verification systems and automatic speech recognition . The proposed approach involves a non-neural algorithm called Random Spectrogram Rhythm Transformation (RSRT) to generate stealthy poisoned utterances by modifying the spectrograms of speech and then reconstructing them using a neural network vocoder . The study seeks to demonstrate the effectiveness and stealthiness of this rhythm trigger in backdoor attacks on speech recognition systems, emphasizing the importance of maintaining the naturalness and intelligibility of the converted speech while achieving a high attack success rate with a low poisoning rate .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel speech backdoor attack method that combines Voice Activity Detection (VAD), RSRT, and neural vocoder techniques to achieve high Automatic Speech Recognition (ASR) performance with a low poisoning rate . The method introduces a trigger that can evade detection by speaker verification systems and ASR, ensuring excellent stealthiness and speech quality . By altering the rhythm or speech components of speech, the paper explores a new approach to speech backdoor attacks, demonstrating efficient and stealthy performance . Additionally, the ablation study in the paper evaluates the ASR performance with different hyper-parameters and poisoned numbers, showcasing the effectiveness of squeezing over stretching in achieving high ASR scores . The experiments in the paper highlight the method's success in achieving high ASR accuracy while maintaining a low poisoning rate, emphasizing the importance of changing speech rhythm and components in backdoor attacks . The proposed speech backdoor attack method in the paper introduces a unique trigger that can evade detection by speaker verification systems and automatic speech recognition, ensuring excellent stealthiness and speech quality . By utilizing Random Spectrogram Rhythm Transformation (RSRT) combined with neural vocoder techniques, the method achieves high Automatic Speech Recognition (ASR) performance with a low poisoning rate . This approach involves altering the rhythm and speech components of speech, providing a new exploratory method for speech backdoor attacks that demonstrates efficiency and stealthiness . The ablation study conducted in the paper evaluates ASR performance with different hyper-parameters and poisoned numbers, highlighting the effectiveness of squeezing over stretching in achieving high ASR scores .

Compared to previous methods, the proposed method stands out due to its ability to maintain very high ASR performance while keeping a low poisoning rate, showcasing excellent stealthiness and efficiency in speech backdoor attacks . The method's trigger can successfully avoid detection by speaker verification systems and ASR, emphasizing the importance of changing speech rhythm and components in achieving effective backdoor attacks . Additionally, the experiments conducted in the paper demonstrate the method's success in achieving high ASR accuracy with minimal poisoning, underlining the significance of rhythm transformation in enhancing attack success rates .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of backdoor attacks on deep learning systems, particularly in the context of speech recognition. Noteworthy researchers in this field include J. Zhang, A. Fu, S. Nepal, H. Kim, T. Gu, K. Liu, B. Dolan-Gavitt, S. Garg, A. Turner, D. Tsipras, A. Madry, J. Dai, C. Chen, Y. Li, X. Pan, M. Zhang, B. Sheng, J. Zhu, M. Yang, T. Zhai, Y. Li, Z. Zhang, B. Wu, Y. Jiang, S.-T. Xia, C. Shi, T. Zhang, Z. Li, H. Phan, T. Zhao, Y. Wang, J. Liu, B. Yuan, Y. Chen, among others .

The key solution mentioned in the paper "Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition" involves a method called Random Spectrogram Rhythm Transformation (RSRT). This non-neural and fast algorithm aims to generate stealthy poisoned utterances by stretching or squeezing the mel spectrograms and then recovering them back to signals. This approach maintains the timbre and content of the speech unchanged, enhancing the stealthiness of the backdoor attack. The experiments conducted with this method demonstrate excellent effectiveness and stealthiness in speech recognition tasks .


How were the experiments in the paper designed?

The experiments in the paper were designed with a specific methodology:

  • The experiments focused on two types of speech recognition tasks: keyword spotting (KWS) and text-independent speech emotion recognition (TSER) .
  • For the KWS task, the Google Speech Commands Dataset v2 was used, containing over 65,000 audios of 35 categories of keywords. The dataset was divided into training, validation, and test sets, with poisoned samples only present in the training set. Four KWS networks were utilized for the experiments .
  • The TSER task utilized the Emotional Speech Dataset (ESD) for emotional speech recognition. The dataset consisted of utterances spoken by native English and Chinese speakers across 5 emotion categories. Signal process deep neural models were employed for the TSER experiments .
  • The experiments aimed to test the stealthiness of poisoned samples through speaker verification and automatic speech recognition. The results demonstrated the effectiveness and stealthiness of the proposed method .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the Google Speech Commands Dataset v2, which contains more than 65,000 audios of 35 categories of keywords for keyword-spotting devices . The code for the proposed attack pipeline is open source, as the study mentions using the AST system for training victim models and paraformer for evaluation, both of which have open-source implementations .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper proposed a speech backdoor attack method that combines various techniques like VAD, RSRT, and neural vocoder to achieve high ASR with a low poisoning rate . The experiments demonstrated the excellent performance in terms of efficiency and stealthiness of speech backdoor attacks using their method . The proposed trigger was effective in avoiding detection by speaker verification systems and automatic speech recognition, showcasing excellent stealthiness and speech quality .

The experiments conducted in the paper included evaluation metrics on poisoned samples to verify the consistency of speech components. The results showed that the rhythm trigger achieved a high attack success rate with a very low poisoning rate, confirming the effectiveness of the proposed method . The paper's contributions were summarized, highlighting the design of a non-neural rhythm transformation poisoning pipeline that demonstrated good stealthiness and effectiveness in backdoor attacks on speech recognition systems .

Furthermore, the paper conducted three types of evaluation experiments to demonstrate the stealthiness of the proposed trigger. These evaluations focused on detecting timbre consistency by SVS and content consistency by ASR, proving that the poisoned samples were challenging for defenders to detect, thus possessing good stealthiness . The results of the attack effectiveness and stealthiness evaluation supported the scientific hypotheses put forward in the paper, showcasing the success of the proposed method in achieving high attack success rates with minimal poisoning rates .


What are the contributions of this paper?

The contributions of the paper "Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition" include:

  • Designing a non-neural rhythm transformation poisoning pipeline with Random Spectrogram Rhythm Transformation (RSRT) to modify the spectrograms of utterances through stretching or squeezing operations, ensuring naturalness and intelligibility when reconstructed into speech using a neural network vocoder .
  • Conducting backdoor attack experiments on Keyword Spotting (KWS) and Text-Independent Speech Emotion Recognition (TSER) tasks, demonstrating the effectiveness and stealthiness of the proposed trigger .
  • Performing three types of evaluation experiments to validate the stealthiness of the trigger, including detecting timbre consistency by Speaker Verification System (SVS) and content consistency by Automatic Speech Recognition (ASR), proving the difficulty for defenders to identify the poisoned samples and their high stealthiness .

What work can be continued in depth?

Further research in the field of imperceptible rhythm backdoor attacks on speech recognition systems can be expanded in several directions:

  • Exploration of Non-Neural Algorithms: Research can delve deeper into the development and enhancement of non-neural algorithms like Random Spectrogram Rhythm Transformation (RSRT) to improve the stealthiness of data poisoning in speech recognition systems .
  • Enhancing Attack Success Rate: There is potential for further investigation into methods that can increase the attack success rate while maintaining a low poisoning rate, as demonstrated by the proposed rhythm trigger in the research .
  • Investigation of New Trigger Mechanisms: Future studies could focus on exploring and developing new trigger mechanisms that can effectively embed undetectable vulnerabilities in speech recognition systems, ensuring both effectiveness and stealthiness .
  • Evaluation of Different Speech Recognition Tasks: Research can be extended to evaluate the effectiveness and stealthiness of poisoned samples in various speech recognition tasks, such as speaker verification and automatic speech recognition, to gain a comprehensive understanding of the impact of backdoor attacks .
  • Comparison with Existing Backdoor Methods: Further analysis and comparison with existing backdoor methods based on data poisoning can provide insights into the strengths and weaknesses of different approaches, aiding in the development of more robust defense mechanisms against such attacks .
Tables
2
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