Machine-learned models for imperceptible message watermarking in videos

A computer-implemented method for decoding imperceptibly watermarked encoded video data, comprising:

• obtaining, by a computing system comprising one or more computing devices, encoded video data comprising a plurality of encoded video frames, wherein one or more of the plurality of encoded video frames comprises spatial-temporal watermark encoding data representative of a message vector, the spatial-temporal watermark encoding data in the one or more the plurality of encoded video frames describing data in the message vector;
• processing, by the computing system, the encoded video data with a decoder portion of a machine-learned watermark decoding model to obtain a first feature encoding and a second feature encoding of the encoded video data, wherein the first feature encoding comprises first spatial-temporal dimensions, and wherein the second feature encoding comprises second spatial-temporal dimensions different than the first spatial-temporal dimensions;
• determining, by the computing system, first weighting data and second weighting data with a weighting portion of the machine-learned watermark decoding model, wherein the first weighting data corresponds to the first spatial-temporal dimensions and the second weighting data corresponds to the second spatial-temporal dimensions;
• and
• processing, by the computing system, the first feature encoding, the second feature encoding, the first weighting data, and the second weighting data with the machine-learned watermark decoding model to obtain a reconstructed message vector.

One or more tangible, non-transitory computer readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations, the operations comprising:

• obtaining a message vector and video data comprising a plurality of video frames;
• processing the message vector and the video data with a machine-learned watermark encoding model to obtain encoded video data comprising a plurality of encoded video frames, wherein at least one of the plurality of encoded video frames comprises spatial-temporal watermark encoding data representative of the message vector, the spatial-temporal watermark encoding data in the at least one of the plurality of encoded video frames describing data in the message vector;
• processing the encoded video data with a machine-learned discriminator model to obtain a discriminator output that indicates whether the encoded video data includes the spatial-temporal watermark encoding data;
• processing the encoded video data with a machine-learned watermark decoding model to obtain decoded video data and a reconstructed message vector;
• evaluating a loss function that evaluates at least one of:
• the discriminator output;
• a difference between the decoded video data and the video data;
• or
• a difference between the message vector and the reconstructed message vector;
• and
• adjusting, based at least in part on the loss function, one or more parameters of at least one of the machine-learned watermark encoding model, the machine-learned watermark decoding model, or the machine-learned discriminator model.