English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Fake audio detection is expected to become an important research area in thefield of smart speakers such as Google Home, Amazon Echo and chatbots developedfor these platforms. This paper presents replay attack vulnerability ofvoice-driven interfaces and proposes a countermeasure to detect replay attackon these platforms. This paper presents a novel framework to model replayattack distortion, and then use a non-learning-based method for replay attackdetection on smart speakers. The reply attack distortion is modeled as ahigher-order nonlinearity in the replay attack audio. Higher-order spectralanalysis (HOSA) is used to capture characteristics distortions in the replayaudio. Effectiveness of the proposed countermeasure scheme is evaluated onoriginal speech as well as corresponding replayed recordings. The replay attackrecordings are successfully injected into the Google Home device via AmazonAlexa using the drop-in conferencing feature.

Towards Vulnerability Analysis of Voice-Driven Interfaces and Countermeasures for Replay