  The evolution of modern voice controlled devices (VCDs) in recent years has
revolutionized the Internet of Things, and resulted in increased realization of
smart homes, personalization and home automation through voice commands. The
introduction of VCDs in IoT is expected to give emergence of new subfield of
IoT, called Multimedia of Thing (MoT). These VCDs can be exploited in IoT
driven environment to generate various spoofing attacks including the replays.
Replay attacks are generated through replaying the recorded audio of legitimate
human speaker with the intent of deceiving the VCDs having speaker verification
system. The connectivity among the VCDs can easily be exploited in IoT driven
environment to generate a chain of replay attacks (multi-order replay attacks).
Existing spoofing detection datasets like ASVspoof and ReMASC contain only the
first-order replay recordings against the bonafide audio samples. These
datasets can not offer evaluation of the anti-spoofing algorithms capable of
detecting the multi-order replay attacks. Additionally, these datasets do not
capture the characteristics of microphone arrays, which is an important
characteristic of modern VCDs. We need a diverse replay spoofing detection
corpus that consists of multi-order replay recordings against the bonafide
voice samples. This paper presents a novel voice spoofing detection corpus
(VSDC) to evaluate the performance of multi-order replay anti-spoofing methods.
The proposed VSDC consists of first and second-order-replay samples against the
bonafide audio recordings. Additionally, the proposed VSDC can also be used to
evaluate the performance of speaker verification systems as our corpus includes
the audio samples of fifteen different speakers. To the best of our knowledge,
this is the first publicly available replay spoofing detection corpus
comprising of first-order and second-order-replay samples.
