Audio deepfake detection is an emerging active topic. A growing number of literatures have
aimed to study deepfake detection algorithms and achieved effective performance, the …

Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

Artefacts that differentiate spoofed from bona-fide utterances can reside in specific temporal
or spectral intervals. Their reliable detection usually depends upon computationally …

The performance of spoofing countermeasure systems depends fundamentally upon the use
of sufficiently representative training data. With this usually being limited, current solutions …

Current text-to-speech algorithms produce realistic fakes of human voices, making deepfake
detection a much-needed area of research. While researchers have presented various …

Advancements in AI-synthesized human voices have created a growing threat of
impersonation and disinformation, making it crucial to develop methods to detect synthetic …

This paper introduces RawBoost, a data boosting and augmentation method for the design
of more reliable spoofing detection solutions which operate directly upon raw waveform …

Thanks to recent advances in deep leaning, sophisticated generation tools exist, nowadays,
that produce extremely realistic synthetic speech. However, malicious uses of such tools are …

Text-to-Speech (TTS) technology brings significant advantages, such as giving a voice to
those with speech impairments, but also enables audio deepfakes and spoofs. The former …

The first spoofing-aware speaker verification (SASV) challenge aims to integrate research
efforts in speaker verification and anti-spoofing. We extend the speaker verification scenario …