Witnessd: Proof-of-process via Adversarial Collapse

TL;DR

Witnessd introduces a novel proof-of-process system that uses jitter seals and layered trust boundaries to provide falsifiable evidence of document authorship, addressing limitations of traditional digital signatures.

Contribution

It presents a new architecture combining jitter seals, delay functions, and trust layers to produce verifiable, falsifiable evidence of document creation process.

Findings

Deterministic rejection of invalid proofs in 31,000 trials

Layered trust boundaries complicate forgery attempts

System converts vague doubt into falsifiable allegations

Abstract

Digital signatures prove key possession, not authorship. An author who generates text with AI, constructs intermediate document states post-hoc, and signs each hash produces a signature chain indistinguishable from genuine composition. We address this gap between cryptographic integrity and process provenance. We introduce proof-of-process, a primitive category for evidence that a physical process, not merely a signing key, produced a digital artifact. Our construction, the jitter seal, injects imperceptible microsecond delays derived via HMAC from a session secret, keystroke ordinal, and cumulative document hash. Valid evidence requires that real keystrokes produced the document through those intermediate states. We propose the Adversarial Collapse Principle as an evaluation criterion: evidence systems should be judged by whether disputing them requires a conjunction of specific, testable allegations against components with independent trust assumptions. We present Witnessd, an architecture combining jitter seals with Verifiable Delay Functions, external timestamp anchors, dual-source keystroke validation, and optional hardware attestation. Each layer forces allegations at different capability levels; disputing authentic evidence requires coordinated claims across independent trust boundaries. The system does not prevent forgery: a kernel-level adversary can defeat it, and typing AI-generated content produces valid evidence. The contribution is converting vague doubt into falsifiable allegations. We evaluate across 31,000 verification trials with deterministic rejection of invalid proofs.