University of Florida Research Foundation Inc

Logan E Blue

Patrick G Traynor

Luis Vargas

Bradley G Reaves

Hadi Abdullah

Kevin R Butler

Thomas Shrimpton

Cassidy Gibson

Jessica Nicole Odell

Kevin S Warren

A method is provided for identifying synthetic 'deep-fake' audio samples versus organic audio samples. Methods may include: generating a model of a vocal tract using one or more organic audio samples from a user; identifying a set of bigram-feature pairs from the one or more audio samples; estimating the cross-sectional area of the vocal tract of the user when speaking the set of bigram-feature pairs; receiving a candidate audio sample; identifying bigram-feature pairs of the candidate audio sample that are in the set of bigram-feature pairs; calculating a cross-sectional area of a theoretical vocal tract of a user when speaking the identified bigram-feature pairs; and identifying the candidate audio sample as a deep-fake audio sample in response to the calculated cross-sectional area of the theoretical vocal tract of a user failing to correspond within a predetermined measure of the estimated cross sectional area of the vocal tract of the user.

Detecting deep-fake audio through vocal tract reconstruction

Methods and apparatuses for providing cryptographic authentication within a voice channel are disclosed. The methods and apparatuses can provide cryptographic authentication solely within a voice channel or can use a combination of a voice channel and another data channel. A method for providing cryptographic authentication within a voice channel can operate between telephonic systems and be suitable for operating over G.711/PCMu, AMR and SPEEX&#x2122; codecs, and suitable for operating over mobile, PSTN, and VOIP networks. The method can include providing a modem that is codec agnostic and suitable for executing a TLS-based authentication protocol. The method can include using frequency-shift modulation within a frequency range of 300-3400 Hz.

Practical end-to-end cryptographic authentication for telephony over voice channels

A system for distinguishing between a human voice generated command and an electronic speaker generated command is provided. An exemplary system comprises a microphone array for receiving an audio signal collection, preprocessing circuitry configured for converting the audio signal collection into processed recorded audio signals, energy balance metric determination circuitry configured for calculating a final energy balance metric based on the processed recorded audio signals, and energy balance metric evaluation circuitry for outputting a command originator signal based at least in part on the final energy balance metric.

Method and apparatus for differentiating between human and electronic speaker for voice interface security

Systems and methods for call authentication are provided. A method can include an enrollment protocol that ensures users control the number they claim to own, a handshake protocol that mutually authenticates the calling parties, and a call integrity protocol that ensures the security of the voice channel and the content it carries. A server can act as either an endpoint or intermediary between user clients and client-server architecture can be employed. All protocols can include end-to-end cryptography and the enrollment protocol can issue a certificate that binds the identity of the client to a phone number.

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Logan E Blue