robustnesslab-ai / ATTACK_CATALOG.md
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Perturbation catalogue & invariance assumptions

ID Transformation Intended use Invariance assumption
original No change Baseline, every source ID Known original gold labels
format Uppercase, surrounding quotes, punctuation suffix Surface robustness; case insensitive engine Low-risk, still not human validated
typo Deterministically transpose two internal characters of an eligible token Spelling noise stability Typically low semantic impact, not guaranteed
synonym One predefined language-specific word replacement Small lexical sensitivity check Replacement may shift nuance; only changed rows are scored
context Append one neutral-sounding sentence about research context Test sensitivity to additional context May change rhetorical emphasis and true class
truncate Keep ~65% of source words Destructive information loss NOT stable: no transformed accuracy or accuracy-delta claim

Coverage details

  • If a dictionary replacement cannot match, the text and prediction are unchanged and remain in attempted-row denominator for flipRate.
  • Modified-subset accuracy is computed on actually modified rows only; compare to original accuracy for the same IDs, not all source examples.
  • This tool does not claim that a statistical flip is automatically an adverse effect. Meaning-preservation assumptions require independent annotation.
  • Test randomness is not used in the perturbations. Every transformation is deterministic and reproducible from the same inputs.
  • No external paraphrasing model or paid API is invoked.