The Indistinguishability Threshold: Measuring Cognitive Vulnerabilities to AI-Generated Disinformation

The proliferation of Generative AI has precipitated an “Authenticity Crisis,” where synthetic media effectively breaches the “Indistinguishability Threshold” of human perception. This dissertation presents an integrated Design Science framework to quantify this systemic vulnerability across nine chapters. We introduce three open-source artifacts: RogueGPT, a parametric stimulus engine for generating controlled synthetic news; JudgeGPT, a dual-axis perception platform measuring both authenticity and credibility judgments; and Origin Lens, a mobile application implementing C2PA cryptographic provenance verification. Our empirical pipeline reveals four core findings: (1) human detection accuracy for AI-generated news converges to random chance (∼ 50%), independent of age or digital literacy—challenging the “digital native” assumption; (2) a significant “Fatigue Effect” causes detection capacity to degrade under volume, supporting the “Firehose of Falsehood” strategy; (3) a measurable “Liar’s Dividend” where authentic content is dismissed as AI-generated; and (4) expert practitioners express skepticism toward detection tools and preference for provenance-based standards [25]. Building on the CSET RICHDATA and DISARM frameworks, we validate an adapted “Disinformation Kill Chain” using empirical perception data to identify stage-specific countermeasures. Our findings establish that mitigation must shift from “post-delivery” fact-checking to “pre-delivery” provenance and “post-exposure” cognitive inoculation. This work connects technical AI capabilities with human factors research, contributing evidence-based strategies for platform governance in the Web Science domain.