Automatic adjustment of device volume based on context

The output volume of a device is usually controlled as a fixed value between zero to a maximum. Unless manually adjusted by a user, the volume of the device output does not vary. In the case of speech output, such constant-volume responses can sound unnatural because human speech typically involves nuanced variations in volume that serve as subtle communicative signals. This disclosure describes techniques to dynamically and automatically adjust the volume of devices such as smartphones, smart speakers, smart appliances, etc. The adjustment is achieved by determining the appropriate level for output volume such that a fixed signal-to-noise (SNR) ratio is maintained. Further, if the user permits, parameters related to the user’s context can be used to determine adjustment of the SNR to fit the user’s mood. The techniques can be implemented via a trained machine learning model

Determining Priority Value of Processes Based on Usage History

Generally, the present disclosure is directed to determining optimal priority values for one or more processes in a computing system. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an optimal priority value for a process based on system data and/or process data

Enhancing Privacy of Smart Speakers Using Image Recognition and Motion Sensors

Smart speakers provide a convenient, often hands-free way for a user to interface with smart technology. For instance, if a user receives an email, text message, or other communication, the communication can be played aloud via the smart speaker. However, it may not be desirable for a communication containing personal or otherwise sensitive information to be read aloud. As such, systems and methods are needed that can provide enhanced privacy of smart speakers while maintaining expected functionality of smart speakers. Furthermore, authenticating a user based on audio alone can be challenging and may be less secure than other forms of authentication

Dynamic adjustment of hotword detection threshold

Many devices allow users to speak a hotword to activate the device, e.g., a virtual assistant application, which then responds to the user command. With user permission, incoming speech data is analyzed to determine whether a hotword was uttered. First, coarse hotword detection is performed. If coarse detection indicates that the hotword was spoken, fine hotword detection is performed to confirm that the hotword was spoken. Per techniques described herein, when fine hotword detection is unsuccessful, the threshold for fine hotword detection is reduced for a short time window. Such reduction improves the likelihood of recognition of the next utterance of the hotword, and can reduce consecutive false negatives. Further, the response from the device is adjusted to improve user experience

Defending against attacks on biometrics-based authentication

Many devices include biometrics-based user authentication in addition to secret-based authentication. While secret-based authentication involves a precise match with the known secret, biometrics-based authentication involves fuzzy matching that verifies that the input is similar to known biometrics within an acceptable threshold level of difference. As a result, biometrics-based authentication techniques are susceptible to attacks in which a malicious actor attempts to authenticate as the user via biometrics data that is crafted carefully to be similar to the stored biometrics within the threshold. The techniques of this disclosure guard against such attacks by use of a generative adversarial network (GAN) where random perturbation is added to the received biometrics input for a dynamically determined number of test iterations. The matching threshold value and the number of test iterations can be dynamically determined. If the authentication test during each of the iterations is passed by the perturbed biometrics input, the user providing the biometrics input is authenticated. Otherwise, the device falls back to secret-based authentication

Verifying authenticity of digital images using digital signatures

This disclosure describes techniques to verify the authenticity of digital images using digital signatures. A secure element on a client device generates a public-private key pair and the public key is uploaded to a server. When a new image is captured by the client device, the device hashes the image and signs the hashed result using the private key to form a digital signature. Both the original image and digital signature are uploaded to the server. The server hashes the original image and compares it to the digital signature that has been decrypted using the appropriate stored public key. A match indicates a verified image and the server provides an indication of the verified status of the image to a requesting device along with the image. The described techniques thus enable images to be authenticated as to source and content

Imsi-Catcher Detection For Mobile Operating Systems

Systems and methods for detecting and notifying a mobile device user of an IMSI-catcher are disclosed. The system includes a mobile device installed with an application that collects signal data and wirelessly transmits the data to a server. The application may be part of a remote attestation service and communicates directly with radio interface layer (RIL) to collect telephony network and diagnostic information data from the device’s baseband. The collected data is sent to the server for remote attestation. The method includes detecting anomalies in the signal data from crowd-sourced heuristics and notifying the device user discreetly of the presence of an IMSI-catcher. The system and method provide identification of IMSI catchers with high reliability since the detection is done using crowd-sourced data from many devices and on the server-side, it’s more difficult for an attacker to conceal being identified and to counter the detection

Reinforcement Learning for Fuzzing Testing Techniques

Generally, the present disclosure is directed to using machine learning to manage a trade-off between exploration and exploitation in a fuzzer system. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict inputs that will cause a system being tested to malfunction or crash based on input data provided to the system and output from the system

Health Analyzing Smart Mirror

It can be desirable to monitor an individual’s health over time. For instance, an individual may wish to keep track of his or her weight, percent body fat, caloric intake, heart rate, water percentage, sleep effectiveness, and many other health metrics. A “smart mirror” is disclosed herein that can assist an individual in monitoring these and other health metrics

IoT Device for Vehicle Analytics and Smart Actions

It can be desirable to monitor and/or collect a historical record of vehicle health and/or usage. For instance, the data can be used for fleet management, tax records, improving mapping systems, or other important uses. An IoT-connected device is disclosed herein that can connect to an analytic port in a vehicle, such as an OBD-II port, and provide smart capabilities for the vehicle