Selective content masking for collaborative computing

A computing system, comprising:

• one or more processors;
• and
• one or more non-transitory, computer-readable media that store instructions that when executed by the one or more processors cause the computing system to perform operations, the operations comprising:
• receiving a request from a first participant in a video conference to share with one or more additional participants of the video conference a first content stream within a videoconferencing application;
• converting at least a portion of the first content stream into image data representative of a display of the first content stream;
• inputting the image data representative of the display of the first content stream into a machine-learned model configured for masking sensitive content within shared content;
• processing, by using the machine-learned model, the image data representative of the display of the first content stream to generate a first mask indicative of a region within the first content stream that contains sensitive content;
• and
• receiving from the machine-learned model a first mask indicative of a region within the first content stream that contains sensitive content;
• and
• rendering for the video conference a display of the first content stream that masks the sensitive content based at least in part on the first mask indicative of the region of the first content stream having the sensitive content.

One or more non-transitory computer-readable media that store instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising:

• receiving a first content stream for display to one or more users;
• converting the first content stream into a plurality of image frames including image data representative of at least a portion of the first content stream;
• inputting the plurality of image frames into a machine-learned model configured for masking sensitive content within shared content;
• processing, by using the machine-learned model, the plurality of image frames to generate a first mask indicative of a region within the first content stream that contains sensitive content;
• and
• receiving from the machine-learned model a first mask indicative of a region within the first content stream that contains sensitive content;
• and
• rendering a display of the first content stream that masks the sensitive content based at least in part on the first mask indicative of the region of the first content stream having the sensitive content.