The ASVspoof initiative was created to promote the development of countermeasures which aim to protect automatic speaker verification (ASV) from spoofing attacks. The first community-led, common evaluation held in 2015 focused on countermeasures for speech synthesis and voice conversion spoofing attacks. Arguably, however, it is replay attacks which pose the greatest threat. Such attacks involv...

The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks. In this paper, an extensive analysis on face spoofing detection works published in the last decade is presented. The analyzed works are categorized by their fundamental parts, i.e., descriptors and classifiers. This structured survey also b...

Wireless networks are vulnerable to spoofing attacks, which allows for many other forms of attacks on the networks. In this paper we propose a method for both detecting spoofing attacks, as well as locating the positions of adversaries performing the attacks. We first propose an attack detector for wireless spoofing that utilizes K-means cluster analysis. Next, we describe how we integrated our...

In this paper a novel speaker verification spoofing countermeasure based on analysis of linear prediction error is presented. The method analyses the energy of the prediction error, prediction gain and temporal parameters related to the prediction error signal. The idea of the proposed algorithm and its implementation is described in detail. Various binary classifiers were researched to separat...

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...

Spoofing detection for automatic speaker verification (ASV), which is to discriminate between live and artificial speech, has received increasing attentions recently. However, the previous studies have been done on the clean data without significant noise. It is still not clear whether the spoofing detectors trained on clean speech can generalise well under noisy conditions. In this work, we pe...

Face anti-spoofing is crucial to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classification problem. Many of them struggle to grasp adequate spoofing cues and generalize poorly. In this paper, we argue the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues....

In this paper, we describe a system for detecting spoofing attacks on speaker verification systems. By spoofing we mean an attempt to impersonate a legitimate user. We focus on detecting if the test segment is a far-field microphone recording of the victim. This kind of attack is of critical importance in security applications like access to bank accounts. We present experiments on databases cr...

In biometrics, Fingerprint is widely used in identification of individual’s identity. Biometric recognition is leading technology for identification and security systems. Fingerprint has unique identification among all other biometric modalities. Use of the fingerprints as biometric characteristics is extensively used and developed for fingerprint recognition in forensic, civilian and commercia...

The performance of networks can be severely damaging by the use of wireless spoofing networks. Since they can be easily established, the attacker doesn’t find much hassles in destroying these networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. We will use...