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To what extent can ASV systems naturally defend against spoofing attacks?
Open accessAbstract
The current automatic speaker verification (ASV) task involves making binary decisions on two types of trials: target and non-target. However, emerging advancements in speech generation technology pose significant threats to the reliability of ASV systems. This study investigates whether ASV effortlessly acquires robustness against spoofing attacks (i.e., zero-shot capability) by systematically exploring diverse ASV systems and spoofing attacks, ranging from traditional to cutting-edge techniques. Through extensive analyses conducted on eight distinct ASV systems and 29 spoofing attack systems, we demonstrate that the evolution of ASV inherently incorporates defense mechanisms against spoofing attacks. Nevertheless, our findings also underscore that the advancement of spoofing attacks far outpaces that of ASV systems, hence necessitating further research on spoofing-robust ASV methodologies.
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There is evidence that MaryTTS attacks can be difficult to detect.
“There is also some evidence that they can be difficult to detect (Wu et al., 2015a; Wang et al., 2020; Jung et al., 2024)”
From ASVspoof 5: Design, collection and validation of resources for spoofing, deepfake, and adversarial attack detection using crowdsourced speech (2026) · cites this paper as Jung et al., 2024
Section 5.3 reports that unit-selection-based TTS attacks pose the most significant challenge to the ASV systems tested, supporting the general point that some TTS attacks are difficult to detect. Our copy does not identify those attacks as MaryTTS, so it does not establish the MaryTTS-specific claim. (LLM, conf=0.79)
Experimental — accuracy not yet measuredSuccessful spoofing attacks are not necessarily implemented using the latest techniques and might not require the highest quality synthetic speech to fool a CM or an ASV system.
“However, successful spoofing attacks are not necessarily implemented using the latest techniques, and might not even require the highest quality synthetic speech to fool a CM (Warren et al., 2024) or an ASV system (Jung et al., 2024).”
From ASVspoof 5: Design, collection and validation of resources for spoofing, deepfake, and adversarial attack detection using crowdsourced speech (2026) · cites this paper as Jung et al., 2024
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Metadata
- Authors
- Jung, J.-W., Wang, X., Evans, N., Watanabe, S., Shim, H.-J., Tak, H., Arora, S., Yamagishi, J., & Chung, J. S.
- ORCID
- Venue
- Interspeech 2024
- Publisher
- ISCA
- Published
- 2024-09-01
- Pages
- 3240-3244
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W4402111459
- Cited by
- 7
- References
- 0
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1
- FWCI
- 2.02
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- cc-by
- Locations
- Interspeech (USB) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (2137603, 2138259, 2138296, 2138307, 2138286)
- National Centre for Supercomputing Applications (2138296, #2138307, #2138286, #2138259, #2137603)
- Agence Nationale de la Recherche (ANR-22-CE39-0009)
- Office of Naval Research (N00014)
- Office of Advanced Cyberinfrastructure (#2138286, #2138296, 2138259, #2138307, 2137603)
- Precursory Research for Embryonic Science and Technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Keywords
- speaker verification, spoofing attacks, anti-spoofing, liveness detection, biometric security
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