Training Firewall for Improved Adversarial Robustness of Machine-Learned Model Systems

A computing system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations, the operations comprising:
• obtaining a first dataset comprising first reference inputs and first reference outputs;
• training a first machine-learned model using the first dataset;
• obtaining a second dataset comprising a plurality of second reference inputs, the plurality of second reference inputs obtained from a data corpus based on a distribution of second reference inputs in the second dataset;
• processing, using the first machine-learned model, the plurality of second reference inputs to generate a plurality of second reference outputs corresponding to the plurality of second reference inputs;
• and
• training a second machine-learned model using the plurality of second reference outputs and the plurality of second reference inputs.

One or more non-transitory computer-readable media storing a trained machine-learned model, wherein the trained machine-learned model was trained by:

• obtaining a first dataset comprising first reference inputs and first reference outputs;
• training a first machine-learned model using the first dataset;
• obtaining a second dataset comprising a plurality of second reference inputs, the plurality of second reference inputs obtained from a data corpus based on a distribution of second reference inputs in the second dataset;
• processing, using the first machine-learned model, the plurality of second reference inputs to generate a plurality of second reference outputs corresponding to the plurality of second reference inputs;
• training a second machine-learned model using the plurality of second reference outputs and the plurality of second reference inputs;
• and
• outputting the second machine-learned model as the trained machine-learned model.