A new replay attack against automatic speaker verification systems

Sung Hyun Yoon, Min Sung Koh, Jae Han Park, Ha Jin Yu

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


With the increasing popularity of automatic speaker verification (ASV), the reliability of ASV systems has also gained importance. ASV is vulnerable to various spoofing attacks, especially replay attacks. Thus, recent public competitions and studies based on spoofing attack detection for ASV have mainly focused on the detection of replay attacks. Generally, replayed speech includes the attributes of one playback and two recording devices: the playback device, the recording device used by the attacker, and the recording device embedded in any system to verify input utterances. Therefore, the main attributes differentiating a replayed speech from the genuine speech are the attributes of the playback and the recording devices used by the attacker. In this paper, we propose a novel replay attack and its defense through observation of the general speech-spoofing process. The proposed attack includes only the attribute of one recording device embedded in an ASV system; genuine speech passes through the recording device only once, and the replayed speech produced for the proposed attack passes through the same recording device twice. Because the proposed attack is feasible, it can be considered a new task for replay countermeasures in the training process in order to develop a robust ASV protection system. The experimental results show that this novel replay attack cannot be detected by several of the existing state-of-the-art replay attack detection systems. Furthermore, the new attack can be detected by the same systems successfully if they are retrained with an appropriate dataset designed for the new task.

Original languageEnglish
Article number9000558
Pages (from-to)36080-36088
Number of pages9
JournalIEEE Access
StatePublished - 2020


  • Automatic speaker verification
  • replay attack
  • same recording device
  • spoofing detection


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