A Probabilistic Fusion Framework for Spoofing Aware Speaker Verification

Zhang, You; Zhu, Guangxi; Duan, Zhiyao

doi:10.48550/arxiv.2202.05253

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…The SASV challenge results demonstrate the dependability of today's cutting-edge approaches to spoofing detection and speaker verification. In another work in this domain, [53], Zhang et al develope a probabilistic framework for combining the ASV and countermeasure (CM) subsystem scores. In addition to the probabilistic framework, the authors propose direct inference and fine-tuning strategies, based on the framework, to predict the SASV score.…”

Section: Integrated Solutions (Antispoof and Asv)mentioning

confidence: 99%

Battling voice spoofing: a review, comparative analysis, and generalizability evaluation of state-of-the-art voice spoofing counter measures

et al. 2023

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With the advent of automated speaker verification (ASV) systems comes an equal and opposite development: malicious actors may seek to use voice spoofing attacks to fool those same systems. Various countermeasures have been proposed to detect these spoofing attacks, but current offerings in this arena fall short of a unified solution. For this reason, defensive measures for ASV systems produced in the last 6-7 years need to be classified, and qualitative and quantitative comparisons of state-of-the-art (SOTA) countermeasures should be performed to assess the effectiveness of these systems against real-world attacks. Hence, in this work, we conduct a review of the literature on spoofing detection using hand-crafted features, deep learning, and end-to-end spoofing countermeasure solutions to detect logical access (LA) attacks, such as speech synthesis (SS) and voice conversion (VC), and physical access (PA) attacks, i.e., replay attacks. Additionally, we review integrated and unified solutions to voice spoofing evaluation and speaker verification, and adversarial and anti-forensic attacks on both voice countermeasures and ASV systems. In an extensive experimental analysis, the limitations and challenges of existing spoofing countermeasures are presented, the performance of these countermeasures on several datasets is reported, and cross-corpus evaluations are performed, something that is nearly absent in the existing literature, in order to assess the generalizability of existing solutions. For the experiments, we employ the Voice Spoofing Attacks and Countermeasures ASVspoof2019, ASVspoof2021, and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifiers. (For reproducibility of the results, the code of the testbed can be found at our GitHub Repository * ).

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Section: Integrated Solutions (Antispoof and Asv)mentioning

confidence: 99%

Battling voice spoofing: a review, comparative analysis, and generalizability evaluation of state-of-the-art voice spoofing counter measures

et al. 2023

View full text Add to dashboard Cite

show abstract

Voice Spoofing Attacks and Countermeasures: A Systematic Review, Analysis, and Experimental Evaluation

Khan

Malik

Ryan

et al. 2023

Preprint

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With the advent of automated speaker verification (ASV) systems comes an equal and opposite development: malicious actors may seek to use voice spoofing attacks to fool those same systems. Various countermeasures have been proposed to detect these spoofing attacks, but current offerings in this arena fall short of a unified solution. For this reason, defensive measures for ASV systems produced in the last 6-7 years need to be classified, and qualitative and quantitative comparisons of state-of-the-art (SOTA) countermeasures should be performed to assess the effectiveness of these systems against real-world attacks. Hence, in this work, we conduct a review of the literature on spoof-ing detection using hand-crafted features, deep learning, and end-to-end spoofing countermeasure solutions to detect logical access (LA) attacks, such as speech synthesis (SS) and voice conversion (VC), and physical access (PA) attacks, i.e., replay attacks. Additionally, we review integrated and unified solutions to voice spoofing evaluation and speaker verification, and adversarial and anti-forensic attacks on both voice countermeasures and ASV systems. In an extensive experimental analysis, the limitations and challenges of existing spoofing countermeasures are presented, the performance of these countermeasures on several datasets is reported, and cross-corpus evaluations are performed, something that is nearly absent in the existing literature, in order to assess the gen-eralizability of existing solutions. For the experiments, we employ the 1 Springer Nature 2021 L A T E X template Voice Spoofing Attacks and Countermeasures ASVspoof2019, ASVspoof2021, and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifiers. (For reproducibility of the results, the code of the testbed can be found at our GitHub Repository *).

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A Probabilistic Fusion Framework for Spoofing Aware Speaker Verification

Cited by 2 publications

References 22 publications

Battling voice spoofing: a review, comparative analysis, and generalizability evaluation of state-of-the-art voice spoofing counter measures

Battling voice spoofing: a review, comparative analysis, and generalizability evaluation of state-of-the-art voice spoofing counter measures

Voice Spoofing Attacks and Countermeasures: A Systematic Review, Analysis, and Experimental Evaluation

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