ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems

By Yuan Gong, Jian Yang, Jacob Huber, Mitchell MacKnight, Christian Poellabauer. 2019

47 citations recorded by the source corpus.

This paper introduces a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs). In contrast to prior efforts, the proposed database contains both genuine voice commands and replayed recordings of such commands, collected in realistic VCSs usage scenarios and using modern voice assistant development kits. Specifically, the database contains recordings from four systems (each with a different microphone array) in a variety of environmental conditions with different forms of background noise and relative positions between speaker and device. To the best of our knowledge, this is the first publicly available database that has been specifically designed for the protection of state-of-the-art voice-controlled systems against various replay attacks in various conditions and environments.

Subfields: Artificial Intelligence, Speech & Audio, Privacy & Security, Signal Processing

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Citations in the corpus

Online Adversarial Attacks (2021, background): “To the best of our knowledge, the only existing approaches that craft adversarial examples on streaming data [Gong et al., 2019a, Lin et al., 2017, Sun et al., 2020] require multiple passes through a data stream and thus cannot be applied in a realistic online setting where an adversary is forced…”