Evidence-Consistent Generative Detection under Scenario-Level Distribution Shift
Conventional in-distribution evaluation can overestimate robustness when training and test data share recurring task-specific patterns or surface cues. This risk is especially relevant in social-engineering fraud detection, where attackers can preserve malicious intent while changing the scenario, impersonated entity, or wording. We study this problem as scenario-level out-of-distribution (SL-OOD) detection for SMS and voice phishing, where entire attack scenarios are held out from training while the label space remains fixed. This setting tests whether models can generalize to unseen attack s
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- PossiblePossibly related (embedding) · 51%How Amazon Bedrock catches AI-generated phishing →
- FuzzySimilar title/name (fuzzy) · 84%GoogleCloudPlatform/generative-ai →
“Fuzzy title match (0.92): “Evidence-Consistent Generative Detection under Scenario-Leve” ≈ “GoogleCloudPlatform/generative-ai””
- FuzzySimilar title/name (fuzzy) · 59%steven2358/awesome-generative-ai →
“Fuzzy title match (0.73): “Evidence-Consistent Generative Detection under Scenario-Leve” ≈ “steven2358/awesome-generative-ai””
- LinkedLinked via arxiv author · 85%San Kim →
“Evidence-Consistent Generative Detection under Scenario-Level Distribution Shift”
- LinkedLinked via arxiv author · 85%JinYeong Bak →
“Evidence-Consistent Generative Detection under Scenario-Level Distribution Shift”