Spoof detection using timedelay shallow neural network and feature switching. Detecting spoofed utterances is a fundamental problem in voicebased biometrics. Spoofing can be performed either by logical accesses like speech synthesis, voice conversion or by physical accesses such as replaying the prerecorded utterance. Inspired by the stateoftheart emphxvector based speaker verification approach, this paper proposes a timedelay shallow neural network (TDSNN) for spoof detection for both logical and physical access. The novelty of the proposed TDSNN system visavis conventional DNN systems is that it can handle variable length utterances during testing. Performance of the proposed TDSNN systems and the baseline Gaussian mixture models (GMMs) is analyzed on the ASVspoof2019 dataset. The performance of the systems is measured in terms of the minimum normalized tandem detection cost function (mintDCF). When studied with individual features, the TDSNN system consistently outperforms the GMM system for physical access. For logical access, GMM surpasses TDSNN systems for certain individual features. When combined with the decisionlevel feature switching (DLFS) paradigm, the best TDSNN system outperforms the best baseline GMM system on evaluation data with a relative improvement of 48.03% and 49.47% for both logical and physical access, respectively.

Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone * Users via Deep Temporal Features Prevailing user authentication schemes on smartphones rely on explicit user interaction, where a user types in a passcode or presents a biometric cue such as face, fingerprint, or iris. In addition to being cumbersome and obtrusive to the users, such authentication mechanisms pose security and privacy concerns. Passive authentication systems can tackle these challenges by unobtrusively monitoring the userxe2x80x99s interaction with the device. We propose a Siamese Long ShortTerm Memory (LSTM) network architecture for passive authentication, where users can be verified without requiring any explicit authentication step. On a dataset comprising of measurements from 30 smartphone sensor modalities for 37 users, we evaluate our approach on 8 dominant modalities, namely, keystroke dynamics, GPS location, accelerometer, gyroscope, magnetometer, linear accelerometer, gravity, and rotation sensors. Experimental results find that a genuine user can be correctly verified 96.47% a false accept rate of 0.1% within 3 seconds.