Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.

Abstract: Disclosed are systems and methods including software processes executed by a server that implement a machine-learning architecture for audio source tracing for deepfake detection. The computer extracts a feature vector representing features of the input audio signal. The machine-learning architecture includes one or more embedding extractors for extracting one or more feature vectors from the input audio signal. An attribute detector ingests an embedding and scoring layers generate a source-indicating attribute score. A source tracer includes a multi-class classifier to generate a signal source score using the attribute scores and generates a signal source class.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.

Abstract: Example smart panel display apparatus and related methods are disclosed herein. An example apparatus to control a display of an electronic device includes a user presence detector to determine a presence of a user relative to the device based on image data generated by an image sensor of the device. The example apparatus includes a gaze detector to determine a direction of a gaze of the user relative to the image sensor based on the image data. The example apparatus includes a backlight manager to selectively adjust a display brightness based on the presence of the user and the direction of the gaze of the user.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed. In one example, a compute device to manage energy usage and compute performance includes at least one memory, instructions, and processor circuitry. The processor circuitry executes the instructions to determine a system power mode based on first telemetry data associated with the compute device. The processor circuitry executes the instructions to provide user activity data and second telemetry data associated with the compute device to a classification system. The processor circuitry executes the instructions to configure a plurality of parameters to manage power consumption and performance of the compute device based on a classification by the classification system.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed. In one example, a compute device to manage energy usage and compute performance includes at least one memory, instructions, and processor circuitry. The processor circuitry executes the instructions to determine a system power mode based on first telemetry data associated with the compute device. The processor circuitry executes the instructions to provide user activity data and second telemetry data associated with the compute device to a classification system. The processor circuitry executes the instructions to configure a plurality of parameters to manage power consumption and performance of the compute device based on a classification by the classification system.

Abstract: Example smart panel display apparatus and related methods are disclosed herein. An example apparatus to control a display of an electronic device includes a user presence detector to determine a presence of a user relative to the device based on image data generated by an image sensor of the device. The example apparatus includes a gaze detector to determine a direction of a gaze of the user relative to the image sensor based on the image data. The example apparatus includes a backlight manager to selectively adjust a display brightness based on the presence of the user and the direction of the gaze of the user.