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name: imagenet_c_corruption_protocol description: Apply ImageNet-C-style common image corruptions with five severity levels and reproducible metadata for robustness experiments.

ImageNet-C Corruption Protocol

Use this skill when a task needs a bounded, reproducible version of the ImageNet-C corruption protocol for arrays or lightweight image fixtures. Do not use it for adversarial perturbations, training-time augmentation claims, or full ImageNet-C scoring unless the real ImageNet-C assets are available.

Inputs

• Image-like numeric arrays shaped as height x width x channels with values in [0, 1].
• corruption: one of gaussian_noise, shot_noise, defocus_blur, brightness, contrast, pixelate, or jpeg_compression_proxy.
• severity: integer from 1 to 5.
• Optional integer seed for stochastic corruptions.

Outputs

• Corrupted image with the same shape and value range.
• Metadata containing corruption name, severity, seed, and distortion summaries.

Workflow

1. Validate shape, severity, and corruption name.
2. Map the severity to a fixed distortion strength.
3. Apply the corruption without changing labels.
4. Clip the result into [0, 1] and preserve shape.
5. Record distortion metadata for downstream mechanism checks.

Validation

Run:

python tests/test_corruption_protocol.py

Limitations

This skill is mechanism-faithful for reduced recovery but is not a byte-identical implementation of every ImageNet-C corruption. Full benchmark claims require the official ImageNet-C data or exact generation pipeline.