Voice Spoofing Countermeasures: Taxonomy, State-of-the-art, experimental
analysis of generalizability, open challenges, and the way forward
- URL: http://arxiv.org/abs/2210.00417v2
- Date: Mon, 21 Nov 2022 19:29:07 GMT
- Title: Voice Spoofing Countermeasures: Taxonomy, State-of-the-art, experimental
analysis of generalizability, open challenges, and the way forward
- Authors: Awais Khan, Khalid Mahmood Malik, James Ryan, and Mikul Saravanan
- Abstract summary: We conduct a review of the literature on spoofing detection using hand-crafted features, deep learning, end-to-end, and universal spoofing countermeasure solutions.
We report the performance of these countermeasures on several datasets and evaluate them across corpora.
- Score: 2.393661358372807
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Malicious actors may seek to use different voice-spoofing attacks to fool ASV
systems and even use them for spreading misinformation. Various countermeasures
have been proposed to detect these spoofing attacks. Due to the extensive work
done on spoofing detection in automated speaker verification (ASV) systems in
the last 6-7 years, there is a need to classify the research and perform
qualitative and quantitative comparisons on state-of-the-art countermeasures.
Additionally, no existing survey paper has reviewed integrated solutions to
voice spoofing evaluation and speaker verification, adversarial/antiforensics
attacks on spoofing countermeasures, and ASV itself, or unified solutions to
detect multiple attacks using a single model. Further, no work has been done to
provide an apples-to-apples comparison of published countermeasures in order to
assess their generalizability by evaluating them across corpora. In this work,
we conduct a review of the literature on spoofing detection using hand-crafted
features, deep learning, end-to-end, and universal spoofing countermeasure
solutions to detect speech synthesis (SS), voice conversion (VC), and replay
attacks. Additionally, we also review integrated solutions to voice spoofing
evaluation and speaker verification, adversarial and anti-forensics attacks on
voice countermeasures, and ASV. The limitations and challenges of the existing
spoofing countermeasures are also presented. We report the performance of these
countermeasures on several datasets and evaluate them across corpora. For the
experiments, we employ the ASVspoof2019 and VSDC datasets along with GMM, SVM,
CNN, and CNN-GRU classifiers. (For reproduceability of the results, the code of
the test bed can be found in our GitHub Repository.
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