Causal Reality Attestation: A Conceptual Framework for Verifiable Physical Provenance of Digital Media

Abstract

Causal Reality Attestation (CRA) is a conceptual framework for reasoning about whether a digital artifact was produced through a claimed physical interaction, rather than attempting to infer that history from the artifact alone. The paper distinguishes physical-event factuality—the historical question of whether the claimed causal relation actually occurred—from attested physical provenance, the evidence-relative support that a verifier can assign to that claim. It proposes a typed causal-evidence graph, a separate dependency model for shared compromise relations, and Minimum-Control Cost (MCC) as a dependency-sensitive measure of the least modeled control cost sufficient for forgery under an explicit adversary and calibration model. The paper does not claim a new multi-witness architecture, proof-of-location mechanism, or new mathematical optimization technique. Instead, it positions CRA as a conceptual and analytical framework that connects existing work in content provenance, hardware attestation, secure capture, proof-of-location, contextual witnessing, attack modeling, common-cause failure analysis, and related techniques. This is a position paper and research agenda, not a security proof, protocol standard, or empirical evaluation. AI use disclosure. The author used OpenAI ChatGPT, OpenAI Codex, and Anthropic Claude as interactive aids for conceptual discussion, literature discovery, drafting and revision, editorial review, structural critique, document preparation, and reference checking. This assistance affected the wording, organisation, and internal consistency of all sections. All references were checked by the author against primary sources. No AI system is listed as an author or contributor, and no AI-generated empirical data or results are presented as real observations. Full disclosure is in the “AI Use Disclosure” section of the paper.