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Voice Mimicry Attacks Assisted by Automatic Speaker Verification
- Source :
- Computer Speech and Language, Computer Speech and Language, Elsevier, 2019, 59, pp.36-54. ⟨10.1016/j.csl.2019.05.005⟩, Computer Speech and Language, 2019, 59, pp.36-54. ⟨10.1016/j.csl.2019.05.005⟩
- Publication Year :
- 2019
-
Abstract
- In this work, we simulate a scenario, where a publicly available ASV system is used to enhance mimicry attacks against another closed source ASV system. In specific, ASV technology is used to perform a similarity search between the voices of recruited attackers (6) and potential target speakers (7,365) from VoxCeleb corpora to find the closest targets for each of the attackers. In addition, we consider 'median', 'furthest', and 'common' targets to serve as a reference points. Our goal is to gain insights how well similarity rankings transfer from the attacker's ASV system to the attacked ASV system, whether the attackers are able to improve their attacks by mimicking, and how the properties of the voices of attackers change due to mimicking. We address these questions through ASV experiments, listening tests, and prosodic and formant analyses. For the ASV experiments, we use i-vector technology in the attacker side, and x-vectors in the attacked side. For the listening tests, we recruit listeners through crowdsourcing. The results of the ASV experiments indicate that the speaker similarity scores transfer well from one ASV system to another. Both the ASV experiments and the listening tests reveal that the mimicry attempts do not, in general, help in bringing attacker's scores closer to the target's. A detailed analysis shows that mimicking does not improve attacks, when the natural voices of attackers and targets are similar to each other. The analysis of prosody and formants suggests that the attackers were able to considerably change their speaking rates when mimicking, but the changes in F0 and formants were modest. Overall, the results suggest that untrained impersonators do not pose a high threat towards ASV systems, but the use of ASV systems to attack other ASV systems is a potential threat.<br />Published in Computer Speech and Language. arXiv admin note: text overlap with arXiv:1811.03790
- Subjects :
- FOS: Computer and information sciences
Sound (cs.SD)
Computer Science - Machine Learning
Speaker verification
Spoofing attack
Computer Science - Cryptography and Security
Computer science
Speech recognition
Nearest neighbor search
02 engineering and technology
spoofing
Crowdsourcing
01 natural sciences
Computer Science - Sound
Theoretical Computer Science
Machine Learning (cs.LG)
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
prosody
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
Audio and Speech Processing (eess.AS)
0103 physical sciences
0202 electrical engineering, electronic engineering, information engineering
FOS: Electrical engineering, electronic engineering, information engineering
perceptual speaker similarity
Active listening
Prosody
010301 acoustics
business.industry
[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
020206 networking & telecommunications
Human-Computer Interaction
Formant
[INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD]
Mimicry
automatic target speaker selection
crowdsourcing
business
Cryptography and Security (cs.CR)
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Software
mimicry
Electrical Engineering and Systems Science - Audio and Speech Processing
Subjects
Details
- Language :
- English
- ISSN :
- 08852308 and 10958363
- Database :
- OpenAIRE
- Journal :
- Computer Speech and Language, Computer Speech and Language, Elsevier, 2019, 59, pp.36-54. ⟨10.1016/j.csl.2019.05.005⟩, Computer Speech and Language, 2019, 59, pp.36-54. ⟨10.1016/j.csl.2019.05.005⟩
- Accession number :
- edsair.doi.dedup.....088b77ce8fc51614a457c9591c33fa7e