555 research outputs found
Anti-spoofing Methods for Automatic SpeakerVerification System
Growing interest in automatic speaker verification (ASV)systems has lead to
significant quality improvement of spoofing attackson them. Many research works
confirm that despite the low equal er-ror rate (EER) ASV systems are still
vulnerable to spoofing attacks. Inthis work we overview different acoustic
feature spaces and classifiersto determine reliable and robust countermeasures
against spoofing at-tacks. We compared several spoofing detection systems,
presented so far,on the development and evaluation datasets of the Automatic
SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge
2015.Experimental results presented in this paper demonstrate that the useof
magnitude and phase information combination provides a substantialinput into
the efficiency of the spoofing detection systems. Also wavelet-based features
show impressive results in terms of equal error rate. Inour overview we compare
spoofing performance for systems based on dif-ferent classifiers. Comparison
results demonstrate that the linear SVMclassifier outperforms the conventional
GMM approach. However, manyresearchers inspired by the great success of deep
neural networks (DNN)approaches in the automatic speech recognition, applied
DNN in thespoofing detection task and obtained quite low EER for known and
un-known type of spoofing attacks.Comment: 12 pages, 0 figures, published in Springer Communications in Computer
and Information Science (CCIS) vol. 66
Voice Spoofing Countermeasures: Taxonomy, State-of-the-art, experimental analysis of generalizability, open challenges, and the way forward
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
Secure Automatic Speaker Verification Systems
The growing number of voice-enabled devices and applications consider automatic speaker verification (ASV) a fundamental component. However, maximum outreach for ASV in critical domains e.g., financial services and health care, is not possible unless we overcome security breaches caused by voice cloning, and replayed audios collectively known as the spoofing attacks. The audio spoofing attacks over ASV systems on one hand strictly limit the usability of voice-enabled applications; and on the other hand, the counterfeiter also remains untraceable. Therefore, to overcome these vulnerabilities, a secure ASV (SASV) system is presented in this dissertation. The proposed SASV system is based on the concept of novel sign modified acoustic local ternary pattern (sm-ALTP) features and asymmetric bagging-based classifier-ensemble. The proposed audio representation approach clusters the high and low-frequency components in audio frames by normally distributing frequency components against a convex function. Then, the neighborhood statistics are applied to capture the user specific vocal tract information. This information is then utilized by the classifier ensemble that is based on the concept of weighted normalized voting rule to detect various spoofing attacks. Contrary to the existing ASV systems, the proposed SASV system not only detects the conventional spoofing attacks (i.e. voice cloning, and replays), but also the new attacks that are still unexplored by the research community and a requirement of the future. In this regard, a concept of cloned replays is presented in this dissertation, where, replayed audios contains the microphone characteristics as well as the voice cloning artifacts. This depicts the scenario when voice cloning is applied in real-time. The voice cloning artifacts suppresses the microphone characteristics thus fails replay detection modules and similarly with the amalgamation of microphone characteristics the voice cloning detection gets deceived. Furthermore, the proposed scheme can be utilized to obtain a possible clue against the counterfeiter through voice cloning algorithm detection module that is also a novel concept proposed in this dissertation. The voice cloning algorithm detection module determines the voice cloning algorithm used to generate the fake audios. Overall, the proposed SASV system simultaneously verifies the bonafide speakers and detects the voice cloning attack, cloning algorithm used to synthesize cloned audio (in the defined settings), and voice-replay attacks over the ASVspoof 2019 dataset. In addition, the proposed method detects the voice replay and cloned voice replay attacks over the VSDC dataset. Rigorous experimentation against state-of-the-art approaches also confirms the robustness of the proposed research
Protecting Voice Controlled Systems Using Sound Source Identification Based on Acoustic Cues
Over the last few years, a rapidly increasing number of Internet-of-Things
(IoT) systems that adopt voice as the primary user input have emerged. These
systems have been shown to be vulnerable to various types of voice spoofing
attacks. Existing defense techniques can usually only protect from a specific
type of attack or require an additional authentication step that involves
another device. Such defense strategies are either not strong enough or lower
the usability of the system. Based on the fact that legitimate voice commands
should only come from humans rather than a playback device, we propose a novel
defense strategy that is able to detect the sound source of a voice command
based on its acoustic features. The proposed defense strategy does not require
any information other than the voice command itself and can protect a system
from multiple types of spoofing attacks. Our proof-of-concept experiments
verify the feasibility and effectiveness of this defense strategy.Comment: Proceedings of the 27th International Conference on Computer
Communications and Networks (ICCCN), Hangzhou, China, July-August 2018. arXiv
admin note: text overlap with arXiv:1803.0915
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