scholarly journals A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

2021 ◽  
Author(s):  
Choon Beng Tan ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Norazlina Khamis ◽  
Puteri Nor Ellyza binti Nohuddin ◽  
Zuraini Zainol ◽  
...  
Keyword(s):  
Recent Work ◽  
State Of The Art ◽  
Speaker Verification ◽  
Attack Detection ◽  
Systematic Analysis ◽  
Biometric Technology ◽  
The Future ◽  
Future Direction ◽  
Verification Systems ◽  
Presentation Attack Detection

AbstractThe emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the trend analysis of recent work on PAD systems were built and presented, whereby the recent and relevant articles including articles from Interspeech and ICASSP Conferences, mostly indexed by Scopus, published between 2015 and 2021 were considered. A total of 172 articles were surveyed in this work. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established.

scholarly journals Integrated Replay Spoofing-Aware Text-Independent Speaker Verification

2020 ◽  
Vol 10 (18) ◽  
pp. 6292
Author(s):  
Hye-jin Shim ◽  
Jee-weon Jung ◽  
Ju-ho Kim ◽  
Ha-jin Yu
Keyword(s):  
Speaker Identification ◽  
Speaker Verification ◽  
Attack Detection ◽  
Integrated System ◽  
Specific Information ◽  
Modular Approach ◽  
Detection Systems ◽  
Verification Systems ◽  
Monolithic Approach ◽  
Presentation Attack Detection

A number of studies have successfully developed speaker verification or presentation attack detection systems. However, studies integrating the two tasks remain in the preliminary stages. In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection: an end-to-end monolithic approach and a back-end modular approach. The first approach simultaneously trains speaker identification, presentation attack detection, and the integrated system using multi-task learning using a common feature. However, through experiments, we hypothesize that the information required for performing speaker verification and presentation attack detection might differ because speaker verification systems try to remove device-specific information from speaker embeddings, while presentation attack detection systems exploit such information. Therefore, we propose a back-end modular approach using a separate deep neural network (DNN) for speaker verification and presentation attack detection. This approach has thee input components: two speaker embeddings (for enrollment and test each) and prediction of presentation attacks. Experiments are conducted using the ASVspoof 2017-v2 dataset, which includes official trials on the integration of speaker verification and presentation attack detection. The proposed back-end approach demonstrates a relative improvement of 21.77% in terms of the equal error rate for integrated trials compared to a conventional speaker verification system.

scholarly journals Speaker verification system based on articulatory information from ultrasound recordings

DYNA ◽  
2020 ◽  
Vol 87 (213) ◽  
pp. 9-16
Author(s):  
Franklin Alexander Sepulveda Sepulveda ◽  
Dagoberto Porras-Plata ◽  
Milton Sarria-Paja
Keyword(s):  
State Of The Art ◽  
Speaker Verification ◽  
Environmental Noise ◽  
Speech Signals ◽  
Acoustic Information ◽  
Current State ◽  
Verification System ◽  
Vocal Effort ◽  
Ultrasound System ◽  
Verification Systems

Current state-of-the-art speaker verification (SV) systems are known to be strongly affected by unexpected variability presented during testing, such as environmental noise or changes in vocal effort. In this work, we analyze and evaluate articulatory information of the tongue's movement as a means to improve the performance of speaker verification systems. We use a Spanish database, where besides the speech signals, we also include articulatory information that was acquired with an ultrasound system. Two groups of features are proposed to represent the articulatory information, and the obtained performance is compared to an SV system trained only with acoustic information. Our results show that the proposed features contain highly discriminative information, and they are related to speaker identity; furthermore, these features can be used to complement and improve existing systems by combining such information with cepstral coefficients at the feature level.

On contracted forms in Canadian French

1985 ◽  
Vol 15 (1) ◽  
pp. 21-27
Author(s):  
Douglas C. Walker
Keyword(s):  
Recent Work ◽  
Canadian French ◽  
The Future ◽  
Future Direction ◽  
Linguistic Evolution ◽  
Social Profiles

Speculation concerning the future direction of linguistic evolution, despite its obvious pitfalls, has a long tradition, not to mention attraction, in linguistics. In the French domain, one need only think of Pulgram (1967) or of various works by Ashby concerning « Future French » (1974, 1977), both of whom reflect on ideas present in French scholarship dating from the early decades of this century (e.g. Meillet, 1921, 1936; Vendryes, 1923). Recent work in typology, showing apparently orderly progression from one language state to another, has buttressed speculative efforts, as have many of the results of sociolinguistics, where clear trends in age or social profiles often allow one to infer directionality and even end result on the basis of ongoing change.

scholarly journals Integrated Presentation Attack Detection and Automatic Speaker Verification: Common Features and Gaussian Back-end Fusion

2018 ◽  
Author(s):  
Massimiliano Todisco ◽  
Héctor Delgado ◽  
Kong Aik Lee ◽  
Md Sahidullah ◽  
Nicholas Evans ◽  
...  
Keyword(s):  
Speaker Verification ◽  
Attack Detection ◽  
Common Features ◽  
Presentation Attack Detection

scholarly journals Fingerprint Presentation Attack Detection using Referential Quality Metrics and Minutiae Count

2021 ◽  
Author(s):  
Akhilesh Verma ◽  
Anshadha Gupta ◽  
Mohammad Akbar ◽  
Arun Kumar Yadav ◽  
Divakar Yadav
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
State Of The Art ◽  
User Authentication ◽  
Attack Detection ◽  
Classification Error ◽  
The Public ◽  
The Past ◽  
Biometric Systems ◽  
Presentation Attack Detection

Abstract The fingerprint presentation attack is still a major challenge in biometric systems due to its increased applications worldwide. In the past, researchers used Fingerprint Presentation Attack Detection (FPAD) for user authentication, but it suffers from reliable authentication due to less focus on reducing the ‘error rate’. In this paper, we proposed an algorithm, based on referential image quality (RIQ)-metrics and minutiae count using neural network, k-NN and SVM for FPAD. We evaluate and validate the error rate reduction with different machine learning models on the public domain, such as LivDet crossmatch dataset2015 and achieved an accuracy of 88% with a neural network, 88.6% with k-NN and 88.8% using SVM. In addition, the average classification error (ACE) score is 0.1197 for ANN, 0.1138 for k-NN and 0.1117 for SVM. Thus, the results obtained show that it was achieved a reasonable accuracy with a low ACE score with respect to other state-of-the-art methods.

scholarly journals fPADnet: Small and Efficient Convolutional Neural Network for Presentation Attack Detection

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2532 ◽  
Author(s):  
Thi Nguyen ◽  
Eunsoo Park ◽  
Xuenan Cui ◽  
Van Nguyen ◽  
Hakil Kim
Keyword(s):  
Processing Time ◽  
Detection Method ◽  
State Of The Art ◽  
Network Size ◽  
Attack Detection ◽  
Gram Matrix ◽  
Texture Information ◽  
Crucial Problem ◽  
Fingerprint Detection ◽  
Presentation Attack Detection

The rapid growth of fingerprint authentication-based applications makes presentation attack detection, which is the detection of fake fingerprints, become a crucial problem. There have been numerous attempts to deal with this problem; however, the existing algorithms have a significant trade-off between accuracy and computational complexity. This paper proposes a presentation attack detection method using Convolutional Neural Networks (CNN), named fPADnet (fingerprint Presentation Attack Detection network), which consists of Fire and Gram-K modules. Fire modules of fPADnet are designed following the structure of the SqueezeNet Fire module. Gram-K modules, which are derived from the Gram matrix, are used to extract texture information since texture can provide useful features in distinguishing between real and fake fingerprints. Combining Fire and Gram-K modules results in a compact and efficient network for fake fingerprint detection. Experimental results on three public databases, including LivDet 2011, 2013 and 2015, show that fPADnet can achieve an average detection error rate of 2.61%, which is comparable to the state-of-the-art accuracy, while the network size and processing time are significantly reduced.

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