Abstract: Techniques are disclosed for detecting adversarial attacks. A machine learning (ML) system processes the input into and output of a ML model using an adversarial detection module that does not include a direct external interface. The adversarial detection module includes a detection model that generates a score indicative of whether the input is adversarial using, e.g., a neural fingerprinting technique or a comparison of features extracted by a surrogate ML model to an expected feature distribution for the output of the ML model. In turn, the adversarial score is compared to a predefined threshold for raising an adversarial flag. Appropriate remedial measures, such as notifying a user, may be taken when the adversarial score satisfies the threshold and raises the adversarial flag.

Abstract: Various embodiments set forth techniques for positioning speakers within a venue. The techniques include generating, via a machine learning model, at least one of a two-dimensional (2D) representation or a three-dimensional (3D) representation of a venue based on one or more images of the venue. The techniques further include determining one or more parameters associated with one or more speakers to be placed within the venue based on the at least one of the 2D representation or the 3D representation.

Abstract: Techniques are disclosed for detecting adversarial attacks. A machine learning (ML) system processes the input into and output of a ML model using an adversarial detection module that does not include a direct external interface. The adversarial detection module includes a detection model that generates a score indicative of whether the input is adversarial using, e.g., a neural fingerprinting technique or a comparison of features extracted by a surrogate ML model to an expected feature distribution for the output of the ML model. In turn, the adversarial score is compared to a predefined threshold for raising an adversarial flag. Appropriate remedial measures, such as notifying a user, may be taken when the adversarial score satisfies the threshold and raises the adversarial flag.

Abstract: A method of operation of a navigation system includes: determining a designated location for estimating travel information; generating a travel information A for traveling from an designated location to a destination A; generating a travel information B for traveling from the designated location to a destination B; and presenting the travel information A and the travel information B concurrently based on a display priority for displaying on a device.

Abstract: A method of operation of a navigation system includes: determining a designated location for estimating travel information; generating a travel information A for traveling from an designated location to a destination A; generating a travel information B for traveling from the designated location to a destination B; and presenting the travel information A and the travel information B concurrently based on a display priority for displaying on a device.