This paper provides an overview of the Speaker Antispoofing Competition organized by Biometric group at Idiap Research Institute for the IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 2016). The competition used AVspoof database, which contains a comprehensive set of presentation attacks, including, (i) direct replay attacks when a genuine data is played back using a laptop and two phones (Samsung Galaxy S4 and iPhone 3G), (ii) synthesized speech replayed with a laptop, and (iii) speech created with a voice conversion algorithm, also replayed with a laptop.The paper states competition goals, describes the database and the evaluation protocol, discusses solutions for spoofing or presentation attack detection submitted by the participants, and presents the results of the evaluation.

Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UC-HU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources.

El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription AbstractThis paper evaluates the performance of the twelve primary systems submitted to the evaluation on speaker verification in the context of a mobile environment using the MOBIO database. The mobile environment provides a challenging and realistic test-bed for current state-of-the-art speaker verification techniques. Results in terms of equal error rate (EER), half total error rate (HTER) and detection error trade-off (DET) confirm that the best performing systems are based on total variability modeling, and are the fusion of several sub-systems. Nevertheless, the good old UBM-GMM based systems are still competitive. The results also show that the use of additional data for training as well as gender-dependent features can be helpful.

Research in the area of automatic speaker verification (ASV) has advanced enough for the industry to start using ASV systems in practical applications. However, these systems are highly vulnerable to spoofing or presentation attacks (PAs), limiting their wide deployment. Several speechbased presentation attack detection (PAD) methods have been proposed recently but most of them are based on hand crafted frequency or phase-based features. Although convolutional neural networks (CNN) have already shown breakthrough results in face recognition, little is understood whether CNNs are as effective in detecting presentation attacks in speech. In this paper, to investigate the applicability of CNNs for PAD, we consider shallow and deep examples of CNN architectures implemented using Tensorflow and compare their performances with the state of the art MFCC with GMM-based system on two large databases with presentation attacks: publicly available voicePA and proprietary BioCPqD-PA. We study the impact of increasing the depth of CNNs on the performance, and note how they perform on unknown attacks, by using one database to train and another to evaluate. The results demonstrate that CNNs are able to learn a database significantly better (increasing depth also improves the performance), compared to hand crafted features. However, CNN-based PADs still lack the ability to generalize across databases and are unable to detect unknown attacks well.

This paper describes presentation attack detection systems developed for the Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2017). The submitted systems, using calibration and score fusion techniques, combine different sub-systems (up to 18), which are based on eight state of the art features and rely on Gaussian mixture models and feedforward neural network classifiers. The systems achieved the top five performances in the competition. We present the proposed systems and analyze the calibration and fusion strategies employed. To assess the systems' generalization capacity, we evaluated it on an unrelated larger database recorded in Portuguese language, which is different from the English language used in the competition. These extended evaluation results show that the fusion-based system, although successful in the scope of the evaluation, lacks the ability to accurately discriminate genuine data from attacks in unknown conditions, which raises the question on how to assess the generalization ability of attack detection systems in practical application scenarios.