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name: masked_program_embedding description: Build BAR-style masked target embeddings and universal tanh-bounded programs while preserving protected target-domain data.

Masked Program Embedding

Use this skill when implementing adversarial reprogramming or a BAR proxy experiment that must embed smaller target-domain arrays in a larger source-domain input and add a universal program only outside the protected region.

Do not use it for ordinary data augmentation or for perturbations that are allowed to alter the target sample content.

Inputs

• A batch of same-shaped numeric target samples.
• A source canvas shape with the same rank as each sample.
• An embedding offset, or None to center the sample.
• Program parameters W with the source canvas shape.

Outputs

• Embedded batch X.
• Binary mask M, with 0 on target data and 1 on programmable cells.
• Program P = tanh(W*M).
• Programmed batch X + P.

Workflow

1. Validate that every target sample fits inside the source canvas.
2. Place every target sample at the same offset in a zero canvas.
3. Construct a mask that protects exactly the embedded target region.
4. Apply the universal tanh-bounded program outside that region.
5. Verify protected values are unchanged before passing data to a black-box model.

Validation

Run python tests/test_masked_program_embedding.py or validate the skill tree with validate_skill_tree.py --run-tests.

Limitations

This skill does not choose label mappings or optimize W; it only provides the input transformation contract used by BAR.