Oracle International Corporation

Microsoft Technology Licensing, LLC

Guy G Michaeli

Kenny C Gross

Timothy David Cline

Guang Chao Wang

Matthew T Gerdes

Serge Le Huitouze

Stephen J Green

Mandip S Bhuller

Daniel J Janni

Scott D Brothers

Anthony A Valey

Systems, methods, and other embodiments associated with computer distress-call detection and authentication are described. In one embodiment, a method includes detecting a human voice in audio content of a radio signal. Speech is recognized in the human voice to transform the human voice into text and vocal metrics. Feature scores are generated that represent features of the recognized speech based at least in part on the vocal metrics. The human voice is then classified as either a hoax distress call or an authentic distress call based on the feature scores. An alert is then presented indicating that the human voice is one of the hoax distress call or the authentic distress call.

Computerized distress call detection and authentication

Systems, methods, and other embodiments associated with computer deepfake detection are described. In one embodiment, a method includes converting audio-visual content of a person delivering a speech into a set of time series signals. Residual time series signals of residuals that indicate an extent to which the time series signals differ from machine learning estimates of authentic delivery of the speech by the person are generated. Residual values from one synchronous observation of the residual time series signals are placed into an array of residual values for a point in time. A sequential analysis of the residual values of the array is performed to detect an anomaly in the residual values for the point in time. In response to detection of the anomaly, an alert that deepfake content is detected in the audio-visual content is generated.

Deepfake detection using synchronous observations of machine learning residuals

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Seattle, WA, United States of America

Guy G Michaeli