End-to-end watermarking system

A computer implemented method, comprising:

• obtaining an image;
• obtaining a first data item;
• generating, using an encoder machine learning model to which the first data item is provided as input, a first digital watermark that encodes the first data item;
• tiling two or more instances of the first digital watermark to generate a second digital watermark;
• and
• combining the second digital watermark with the image to obtain a watermarked image.

A system, comprising:

• receiving an image;
• determining that a digital watermark is embedded in a portion of the image, wherein the digital watermark is not visually discernible by a human viewer and wherein the digital watermark is generated using an encoder machine learning model;
• in response to determining that the digital watermark is embedded in the portion of the image, obtaining a first data item encoded within the digital watermark embedded in the portion of the image, including:
• predicting, using a distortion detector machine learning model, one or more distortions present in the portion of the image relative to an original version of the portion of the image;
• modifying the portion of the image based on the predicted one or more distortions while preserving the digital watermark embedded in the portion of the image;
• and
• decoding, using a decoder machine learning model, the modified portion of the image to obtain the first data item encoded within the digital watermark, wherein the decoder machine learning model and the encoder machine learning model are jointly trained as part of an end-to-end learning pipeline;
• and
• validating an item depicted in the image based on the first data item.

A non-transitory computer readable medium storing instructions that, when executed by one or more data processing apparatus, cause the one or more data processing apparatus to perform operations comprising:

• receiving an image;
• determining that a digital watermark is embedded in a portion of the image, wherein the digital watermark is not visually discernible by a human viewer and wherein the digital watermark is generated using an encoder machine learning model;
• in response to determining that the digital watermark is embedded in the portion of the image, obtaining a first data item encoded within the digital watermark embedded in the portion of the image, including:
• predicting, using a distortion detector machine learning model, one or more distortions present in the portion of the image relative to an original version of the portion of the image;
• modifying the portion of the image based on the predicted one or more distortions while preserving the digital watermark embedded in the portion of the image;
• and
• decoding, using a decoder machine learning model, the modified portion of the image to obtain the first data item encoded within the digital watermark, wherein the decoder machine learning model and the encoder machine learning model are jointly trained as part of an end-to-end learning pipeline;
• and
• validating an item depicted in the image based on the first data item.