Although current face anti-spoofing methods achieve promising results under intra-dataset testing, they suffer from poor generalization to unseen attacks. Most existing works adopt domain adaptation (DA) or (DG) techniques address this problem. However, the target is often unknown during training which limits utilization of DA methods. DG can conquer by learning invariant features without seein...

Face Anti-Spoofing (FAS) is essential to secure face recognition systems and has been extensively studied in recent years. Although deep neural networks (DNNs) for the FAS task have achieved promising results intra-dataset experiments with similar distributions of training testing data, DNNs' generalization ability limited under cross-domain scenarios different data. To improve ability, hybrid ...

Automatic Speaker Verification (ASV) systems are prone to spoofing attacks of various kinds. In this study, we explore the effects of different features and spoofing algorithms on a state-of-the-art i-vector speaker verification system. Our study is based on the standard dataset and evaluation protocols released as part of the ASVspoof 2015 challenge. We compare how different features perform w...

Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems. Empirically, given an image, model with more consistent output on different views (i.e., augmentations) of this image usually performs better. Motivated by exciting observation, we conjecture that encouraging feature consistency may be promising way to boost FAS models. In paper, explore thoroughly enhancing bot...

The ASVspoof initiative follows on from the first special session in spoofing and countermeasures for automatic speaker verification (ASV) held during the 2013 edition of INTERSPEECH in Lyon, France [1]. The vision behind that first edition was to promote the consideration of spoofing, to encourage the development of anti-spoofing countermeasures and to gather a community to design, collect and...

In this article authors present an application of spatial processing methods for GPS spoofing detection and mitigation. In the first part of this article, a spoofing detection method, based on phase delay measurements, is proposed. Accuracy and precision of phase delay estimation is assessed for various qualities of received signal. Spoofing detection thresholds are determined. Efficiency of th...

The automatic speaker verification spoofing and countermeasures challenge 2015 provides a common framework for the evaluation of spoofing countermeasures or anti-spoofing techniques in the presence of various seen and unseen spoofing attacks. This contribution proposes a system consisting of amplitude, phase, linear prediction residual, and combined amplitude phase-based countermeasures for the...

Abstract Spoofing attacks posed by generating artificial speech can severely degrade the performance of a speaker verification system. Recently, many anti-spoofing countermeasures have been proposed for detecting varying types from synthetic to replay presentations. While there are numerous effective defenses reported on standalone solutions, integration and spoofing detection systems has obvio...

Face anti-spoofing (FAS) plays a vital role in preventing face recognition systems from presentation attacks. Existing datasets lack diversity due to the insufficient identity and insignificant variance, which limits generalization ability of FAS model. In this paper, we propose Dual Spoof Disentanglement Generation (DSDG) framework tackle challenge by “anti-spoofing via generation”. Depending ...