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name: output_remapping description: Aggregate frozen target model outputs into adversarial task labels using one-to-one or many-to-one mappings.

Target-to-Adversarial Label Remapping

Use this skill when a recovery must implement the paper's h_g mapping from original target-model classes to attacker-task classes. It is appropriate for logits or probabilities. Do not use it as a learned classifier head; the mapping is fixed during program optimization.

Inputs

• scores: original target-model logits or probabilities.
• mapping: dictionary from adversarial labels to lists of original class indices.
• reducer: sum or mean for many-to-one aggregation.

Outputs

• Aggregated adversarial-task scores.
• Deterministic predicted adversarial label.
• Mapping validation metadata.

Workflow

Validate that mapped labels are in range, aggregate each label group, then choose the label with maximum aggregate score using lexical label order for ties.

Validation

Run python tests/test_output_remapping.py.

Limitations

This skill does not choose semantic ImageNet labels; it implements and validates a supplied mapping.