Proof of Process (PoP): Forensic Appraisal and Security Model

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Table of Contents

1. Introduction

The value of Proof of Process (PoP) evidence lies in the Verifier's ability to distinguish biological effort from algorithmic simulation. This document provides the normative framework for forensic appraisal, defining the appraisal logic required to generate a Writers Authenticity Report (WAR).¶

2. Jitter Seal: Captured Behavioral Entropy

Verifiers appraisal behavioral entropy (jitter) to establish a biological binding to the document.¶

3. Semantic Event Definitions

Verifiers use canonical semantic events to reconstruct the authorship topology:¶

INS (Insert):

Content addition at specific offsets.¶

DEL (Delete):

Range-based content removal (error correction analysis).¶

MOV (Move):

Content restructuring (high cognitive load indicator).¶

PASTE (Import):

External source integration (AI agent attribution).¶

4. Forensic Assessment Mechanisms

SNR (Signal-to-Noise Ratio) Analysis:

Verifying the 1/f fractal noise signature of human motor signals to detect machine-clocked synthetic injection.¶

Cognitive Load Correlation (CLC):

To defeat high-fidelity deepfake jitter, Verifiers MUST correlate timing cadence with semantic complexity (IKIs for complex vs simple segments).¶

Mechanical Turk Detection:

Analyzing intra-checkpoint correlation (C_intra) to detect "robotic pacing" in automated systems.¶

QR Presence Challenge (OOB-PC):

Bridging the digital-physical gap via secondary physical devices to ensure a human is in the loop.¶

5. Forgery Cost Bounds

Verifiers calculate the quantified economic cost (C_total) required to forge an Evidence Packet:¶

  C_total = C_vdf + C_entropy + C_hardware

• C_vdf: Electricity and pro-rated hardware cost for sequential VDF computation.¶
• C_entropy: Effort required to synthesize biological noise that satisfies SNR and CLC constraints.¶

6. Absence Proofs and Tool Receipts

Specifies the Tool Receipt Protocol for AI agent attribution and the three-type taxonomy for negative evidence claims (Computationally-bound, Monitoring-dependent, and Environmental).¶

7. Privacy and Inclusivity

To mitigate de-anonymization risks, Verifiers MUST support Evidence Quantization. To ensure inclusivity, Verifiers MUST support Accessibility Modes for non-typical authors (e.g., motor disabilities).¶

Author's Address