548 research outputs found
Multi-Level Liveness Verification for Face-Voice Biometric Authentication
In this paper we present the details of the multilevel liveness verification (MLLV) framework proposed for realizing a secure face-voice biometric authentication system that can thwart different types of audio and video replay attacks. The proposed MLLV framework based on novel feature extraction and multimodal fusion approaches, uncovers the static and dynamic relationship between voice and face information from speaking faces, and allows multiple levels of security. Experiments with three different speaking corpora VidTIMIT, UCBN and AVOZES shows a significant improvement in system performance in terms of DET curves and equal error rates(EER) for different types of replay and synthesis attacks
Detecting replay attacks in audiovisual identity verification
We describe an algorithm that detects a lack of correspondence between speech and lip motion by detecting and monitoring the degree of synchrony between live audio and visual signals. It is simple, effective, and computationally inexpensive; providing a useful degree of robustness against basic replay attacks and against speech or image forgeries. The method is based on a cross-correlation analysis between two streams of features, one from the audio signal and the other from the image sequence. We argue that such an algorithm forms an effective first barrier against several kinds of replay attack that would defeat existing verification systems based on standard multimodal fusion techniques. In order to provide an evaluation mechanism for the new technique we have augmented the protocols that accompany the BANCA multimedia corpus by defining new scenarios. We obtain 0% equal-error rate (EER) on the simplest scenario and 35% on a more challenging one
Authentication of Students and Students’ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11
Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are students’ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a student’s work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio
Detecting replay attacks in audiovisual identity verification
We describe an algorithm that detects a lack of correspondence between speech and lip motion by detecting and monitoring the degree of synchrony between live audio and visual signals. It is simple, effective, and computationally inexpensive; providing a useful degree of robustness against basic replay attacks and against speech or image forgeries. The method is based on a cross-correlation analysis between two streams of features, one from the audio signal and the other from the image sequence. We argue that such an algorithm forms an effective first barrier against several kinds of replay attack that would defeat existing verification systems based on standard multimodal fusion techniques. In order to provide an evaluation mechanism for the new technique we have augmented the protocols that accompany the BANCA multimedia corpus by defining new scenarios. We obtain 0% equal-error rate (EER) on the simplest scenario and 35% on a more challenging one
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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