Microsoft Technology Licensing, LLC

Skype Limited

David Yuheng Zhao

Ming-Chieh Lee

Renat Vafin

Sriram Srinivasan

Andrei Jefremov

Mu Han

Zhuangfei Wu

Qingfen Lin

Ian Anderson

Amy Lu

Henrik Valdemar Turbell

Sergey Sablin

Sergey Silkin

Mattias D Nilsson

Matthieu Michel Jean-Alexis Moinard

Victor Miguel Barbero

Mattias Dan Nilsson

Konrad Hofbauer

Magnus Hemmendorff

Pontus Carlsson

Sergei Nikiforov

Apparatus and methods are disclosed for using machine learning models with private and public domains. Operations can be applied to transform input to a machine learning model in a private domain that is kept secret or otherwise made unavailable to third parties. In one example of the disclosed technology, a method includes applying a private transform to produce transformed input, providing the transformed input to a machine learning model that was trained using a training set modified by the private transform, and generating inferences with the machine learning model using the transformed input. Examples of suitable transforms that can be employed include matrix multiplication, time or spatial domain to frequency domains, and partitioning a neural network model such that an input and at least one hidden layer form part of the private domain, while the remaining layers form part of the public domain.

Protecting deep learned models

Systems and methods may be used for access control. These systems and methods may include using a data processing system to access a video stream, the video stream including an image including a virtual background, segmenting the image into a foreground portion and a background portion to determine whether the foreground portion or the background portion of the image meets a threshold requirement, and outputting an alert in response to determining that the foreground portion or the background portion of the image fails to meet the threshold requirement.

Image security using segmentation

Techniques are described for efficiently embedding frame masks in a video stream. In some solutions, a computer implemented method includes operations for encoding a frame of video data comprising an array of pixels to generate an encoded video frame, and transmitting the encoded video frame to a video decoder. The array of pixels include foreground pixels and background pixels. The foreground pixels have respective first chroma component values which are bounded within a first chroma component range. Encoding the frame of video data can include converting the first chroma component values of the foreground pixels to second chroma component values by applying a mapping function. The second chroma component values are bounded within a second chroma component range. The second chroma component range is shifted or compressed from the first chroma component range.

Embedding frame masks in a video stream

Growing community of inventors

Redmond, WA, United States of America

David Yuheng Zhao