Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Embodiments described herein provide for evaluating call metadata and certificates of inbound calls for authentication. The computer identifies a service provider indicated by the SPID and/or the ANI (or other identifier) of the metadata and identifies a service provider indicated by the SPID and/or ANI (or other identifier) of the certificate, then compares identities of the service providers and/or compares the data values associated with the service providers (e.g., SPIDs, ANIs). Based on this comparison, the computer determines whether the service provider that signed the certificate is first-party signer (e.g., carrier) for the ANI or a third-party signer that is signing certificates as the first-party signer for the ANI.

Abstract: Embodiments described herein provide for systems and methods for verifying authentic JIPs associated with ANIs using CLLIs known to be associated with the ANIs, allowing a computer to authenticate calls using the verified JIPs, among various factors. The computer builds a trust model for JIPs by correlating unique CLLIs to JIPs. A malicious actor might spoof numerous ANIs mapped to a single CLLI, but the malicious actor is unlikely to spoof multiple CLLIs due to the complexity of spoofing the volumes of ANIs associated with multiple CLLIs, so the CLLIs can be trusted when determining whether a JIP is authentic. The computer identifies an authentic JIP when the trust model indicates that a number of CLLIs associated with the JIP satisfies one or more thresholds. A machine-learning architecture references the fact that the JIP is authentic as an authentication factor for downstream call authentication functions.

Abstract: A failsafe mechanism for installing and removing temporary instrumentation during a runtime of an application. Initially, an application is configured with a baseline set of instrumented components such as methods. Additional instrumentation is then deployed in the application, such as to diagnose a performance problem. The failsafe mechanism ensures that the additional instrumentation is automatically removed, even when there is an interruption in a communication link to the application, a computing device failure, a software failure, or some other type of failure, which renders it impossible to manually roll back the instrumentation from a remote user interface. The failsafe mechanism can be provided using callbacks between the computing devices which detect when a connection is unexpectedly lost or closed. Termination of one callback can cascade to one or more other callbacks. The instrumentation rollback can involve reloading un-instrumented byte code of the application.

Abstract: Techniques for analyzing software in which un-instrumented components can be discovered and conditionally instrumented during a runtime of the software. Initially, software such as an application can be configured with a baseline set of instrumented components such as methods. As the application runs, performance data gathered from the instrumentation may indicate that the performance of some methods is below expectations. To analyze this, any methods which are callable from a method at issue are discovered, such as by inspecting the byte code of loaded classes in a JAVA Virtual Machine (JVM). To limit and focus the diagnosis, the instrumentation which is added to the discovered components can be conditional, so that the instrumentation is executed only in a specified context. The context can involve, e.g., a specified sequence of components in which a discovered component is called, and/or transaction data in which a discovered component is called.

Abstract: Techniques for analyzing software in which un-instrumented components can be discovered and conditionally instrumented during a runtime of the software. Initially, software such as an application can be configured with a baseline set of instrumented components such as methods. As the application runs, performance data gathered from the instrumentation may indicate that the performance of some methods is below expectations. To analyze this, any methods which are callable from a method at issue are discovered, such as by inspecting the byte code of loaded classes in a JAVA Virtual Machine (JVM). To limit and focus the diagnosis, the instrumentation which is added to the discovered components can be conditional, so that the instrumentation is executed only in a specified context. The context can involve, e.g., a specified sequence of components in which a discovered component is called, and/or transaction data in which a discovered component is called.

Abstract: A failsafe mechanism for installing and removing temporary instrumentation during a runtime of an application. Initially, an application is configured with a baseline set of instrumented components such as methods. Additional instrumentation is then deployed in the application, such as to diagnose a performance problem. The failsafe mechanism ensures that the additional instrumentation is automatically removed, even when there is an interruption in a communication link to the application, a computing device failure, a software failure, or some other type of failure, which renders it impossible to manually roll back the instrumentation from a remote user interface. The failsafe mechanism can be provided using callbacks between the computing devices which detect when a connection is unexpectedly lost or closed. Termination of one callback can cascade to one or more other callbacks. The instrumentation rollback can involve reloading un-instrumented byte code of the application.

Abstract: An optical signal switch has improved port isolation and extinction ratio by utilizing cascaded or tandem Mach-Zehnder Interferometer (MZI) with thermo-optical or electro-optical refractive index-modulating electrodes on the MZI arms.