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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Voice spoofing attacks pose a significant threat to automated speakerverification systems. Existing anti-spoofing methods often simulate specificattack types, such as synthetic or replay attacks. However, in real-worldscenarios, the countermeasures are unaware of the generation schema of theattack, necessitating a unified solution. Current unified solutions struggle todetect spoofing artifacts, especially with recent spoofing mechanisms. Forinstance, the spoofing algorithms inject spectral or temporal anomalies, whichare challenging to identify. To this end, we present a spectra-temporal fusionleveraging frame-level and utterance-level coefficients. We introduce a novellocal spectral deviation coefficient (SDC) for frame-level inconsistencies andemploy a bi-LSTM-based network for sequential temporal coefficients (STC),which capture utterance-level artifacts. Our spectra-temporal fusion strategycombines these coefficients, and an auto-encoder generates spectra-temporaldeviated coefficients (STDC) to enhance robustness. Our proposed approachaddresses multiple spoofing categories, including synthetic, replay, andpartial deepfake attacks. Extensive evaluation on diverse datasets(ASVspoof2019, ASVspoof2021, VSDC, partial spoofs, and in-the-wild deepfakes)demonstrated its robustness for a wide range of voice applications.

Frame-to-Utterance Convergence: A Spectra-Temporal Approach for Unified Spoofing Detection