{
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    "schema": "fraud-adversarial/1",
    "archetype": "claim_vs_effect",
    "issued_at": "2026-09-16T19:37:04.994247+00:00",
    "subject": {
      "models": [
        "logistic (gradient, attacked)",
        "xgboost (black-box, transfer target)"
      ],
      "dataset": "ULB MLG 'Credit Card Fraud Detection' (284,807 anonymized European card transactions, Sept 2013)",
      "rows": 284807,
      "fraud_rate": 0.0017
    },
    "claim": "the fraud model is robust to adversarial manipulation",
    "verdict": "falsified",
    "finding": {
      "gap": "adversarial_evasion",
      "attack": "FGSM (Fast Gradient Sign Method), crafted on logistic regression",
      "epsilon": 0.5,
      "effect": "logistic regression 96.391% accurate evades 44/195 of caught fraud; the SAME adversarial samples transfer to a black-box XGBoost and evade 15/179 \u2014 mean applied L-inf perturbation 0.500",
      "detail": "gradient-guided perturbations to the (anonymized) transaction features shift a fraudulent transaction below the detection threshold, and the attack transfers to a model the attacker cannot see inside. Honest scope: the rates are modest (adversarial evasion is harder on a PCA-based fraud model than on the synthetic 'amount' surface it replaced), and this is adversarial-ML evasion, not a realistic fraudster's structuring \u2014 but the transferability to a black-box model is the security-relevant result."
    },
    "record_hash": "e4416bd0834101834d10ebf629806324c861769679e14418fda20066b3ecb17a"
  },
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}