Voice-cloning (VC) systems have seen an exceptional increase in the realism of synthesized speech in recent years. The high quality of synthesized speech and the availability of low-cost VC services have given rise to many potential abuses of this technology such as online smearing campaigns and dissemination of fabricated information etc. A number of detection methodologies have been proposed over the years that can detect voice spoofs with reasonably good accuracy. However, these methodologies are mostly evaluated on clean audio databases, such as Asvspoof 2019. This research aims to evaluate state-of-the-art (SOTA) Audio Spoof Detection approaches in the presence of laundering attacks. In that regard, a new laundering attack database, called ASVspoof Laundering Database, is created. This database is based on the ASVspoof 2019 LA eval database comprising a total of 1388.22 hours of audio recordings. Seven SOTA audio spoof detection approaches are evaluated on this laundered database. The results indicate that SOTA systems perform poorly in the presence of aggressive laundering attacks, especially reverberation and additive noise attacks. This suggests the need for robust audio spoof detection.
