FraudBench shows that current multimodal LLMs and specialized AI-image detectors often fail to spot AI-generated fake damage in refund evidence, with true positive rates frequently below 50% on synthetic subsets while producing false positives on real damage.
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Position paper identifies three risks in the SEO-to-GEO transition and argues for answer-level governance focused on contestability, disclosure, auditing, and aligned metrics.
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FraudBench: A Multimodal Benchmark for Detecting AI-Generated Fraudulent Refund Evidence
FraudBench shows that current multimodal LLMs and specialized AI-image detectors often fail to spot AI-generated fake damage in refund evidence, with true positive rates frequently below 50% on synthetic subsets while producing false positives on real damage.
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Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots
Position paper identifies three risks in the SEO-to-GEO transition and argues for answer-level governance focused on contestability, disclosure, auditing, and aligned metrics.