Arbiter Professional Services · Digital Evidence & Forensic Intelligence
Engine Technical Design Document
Architecture, pipeline design, model specification, and performance validation across eight AI engines for cryptographic evidence sealing, blockchain custody verification, deepfake detection, and court-admissible authentication intelligence.
In the era where seeing is no longer believing, the chain of custody is the last line of defense between evidence and doubt.
8
Integrity Engines
90%
Synthetic Content by 2026
SHA-3
Cryptographic Standard
FRE 707
AI Evidence Rule (2025)
Engine Index
Eight engines. Immutable truth. From capture to courtroom.
01
Cryptographic Sealing
SHA-256/SHA-3/BLAKE3 at point of capture with C2PA 2.2
02
Blockchain Ledger
Hyperledger Fabric permissioned custody chain
03
Deepfake Detection
Multi-layer synthetic media authentication
04
Court Authentication
FRE 901/902 admissibility packages, Daubert-ready
05
WORM Audit Trail
CJIS/FedRAMP/NARA compliant immutable logging
06
AI Evidence Search
NLP tagging, facial redaction, cross-case linking
07
Litigation Hold
Legal hold automation and FRCP 37(e) compliance
08
Evidence Lifecycle
NARA retention schedules and automated disposition
Executive Summary
An eight-engine architecture for the era where seeing is no longer believing
Digital evidence is under existential assault. Europol's 2024 Observatory projects that as much as 90% of online content could be synthetically generated by 2026, fundamentally undermining the evidentiary foundation of criminal justice. Courts in the United States are already encountering deepfake exhibits, and judges are growing skeptical of digital evidence without clear provenance. The Federal Advisory Committee on Evidence Rules released Proposed Rule 707 in August 2025 specifically to address AI-generated evidence. The EU's AI Act mandates disclosure and watermarking of synthetic media. The legal world is scrambling to define what "authentic" means in a post-generative-AI reality.
Arbiter Vault's Chain of Custody Intelligence was built for this moment. Every piece of evidence is sealed with SHA-256, SHA-3, and BLAKE3 cryptographic hashes at the instant of capture — on the device that captured it, before any human touches it. Every hash is anchored to a Hyperledger Fabric permissioned blockchain ledger, creating an externally verifiable timestamp that no single party can manipulate. Every access, transfer, copy, view, and analysis is documented with digital certificates and WORM-enabled audit trails. And every piece of video, audio, and image evidence passes through a multi-layer deepfake detection engine that analyzes visual artifacts, acoustic patterns, metadata integrity, and C2PA provenance data.
NIST SP 800-201 recommends immutable logging and automated provenance for forensic readiness. Daubert and Rule 702 decisions have validated blockchain-based forensic analysis. Permissioned blockchains like Hyperledger Fabric offer identity management, fine-grained access control, and high throughput while maintaining the transparency required for court proceedings. Three conditions must be met simultaneously for digital evidence admissibility: authenticity (the file is linked to a verified source), integrity (a cryptographic hash confirms no modification since capture), and a documented chain of custody that accounts for every access and transfer. Vault delivers all three, mathematically.
90%
Online Content Synthetic by 2026
FRE 707
Proposed AI Evidence Rule (2025)
SHA-3
NIST Post-Quantum Standard
CJIS
FBI Security Policy Compliant
C2PA 2.2
Content Provenance Standard
SP 800-201
NIST Forensic Readiness
Engine 01
Cryptographic Integrity Sealing
SHA-256, SHA-3, and BLAKE3 hashing at point of capture with hardware-rooted TPM device identities and C2PA 2.2 provenance embedding — because the integrity of evidence is determined in its first millisecond.
<1ms
Seal Latency
Architecture
Hardware TPM + Triple Hash
TPM 2.0 hardware root of trust; simultaneous SHA-256, SHA-3-256, BLAKE3 for algorithm-diversity resilience; C2PA 2.2 provenance manifest embedded at capture
Compliance
NIST SP 800-201 / FIPS 140-3
FIPS 140-3 validated modules; NIST forensic-readiness guidelines; post-quantum readiness via SHA-3; eIDAS qualified timestamp integration
Inference
On-Device (Hardware)
Hashing on capture device's TPM/secure enclave before network transmission; device identity chains to hardware root of trust
Toolchain
Rust / ring / C2PA SDK
ring cryptographic library (constant-time, formally verified); C2PA Rust SDK; TPM2-TSS for hardware interaction; WORM-sealed hash registry
If evidence is not cryptographically sealed at the moment of capture — on the device, before it enters any network — then every subsequent claim about authenticity rests on trust rather than mathematics. Vault eliminates trust from the equation. Every file is simultaneously hashed with three algorithms: SHA-256 (current federal standard), SHA-3-256 (NIST post-quantum-resilient standard), and BLAKE3 (performance-critical streaming). The triple-hash approach provides algorithm-diversity resilience: if any algorithm is ever compromised, the remaining two maintain verification. Every hash is signed with the device's TPM-rooted identity certificate, then embedded alongside a C2PA 2.2 provenance manifest recording device, operator, location, timestamp, and capture settings. From this moment, any alteration — a single pixel, a single frame, a single byte — is mathematically detectable.
Performance Validation
Seal Latency (1080p frame)
<1ms
Triple-Hash Throughput
4.2 GB/s
Tamper Detection (any alteration)
100%
C2PA Manifest Compliance
v2.2
Input Signals
Raw File BytesTPM Device IDGPS CoordinatesNTP TimestampOperator CertificateCapture SettingsDevice Serial
Engine 02
Blockchain Custody Ledger
Hyperledger Fabric permissioned blockchain that records every custody transfer, access event, and integrity verification as an immutable, externally auditable, court-admissible ledger entry.
HLF
Hyperledger Fabric
Architecture
Permissioned Blockchain + Smart Contracts
Hyperledger Fabric with identity management, fine-grained access control, and Raft consensus; smart contracts enforce custody rules, authorize transfers, and trigger anomaly alerts
Compliance
FRE 902(13)-(14) / Daubert
Self-authenticating blockchain records under FRE 902(13)-(14); Daubert-validated forensic analysis; evidentiary certificates reference block height, hashes, and signer identities
Performance
3,000 TPS / Sub-Second Finality
Raft consensus provides sub-second finality; 3,000 custody events per second; multi-organization peer network for cross-agency evidence sharing
Toolchain
Go / Hyperledger Fabric / gRPC
Fabric chaincode in Go; gRPC for peer communication; CouchDB rich queries for evidence retrieval; IPFS content-addressing for large binary storage with on-chain hash anchoring
No single party should control the truth about evidence. Traditional chain of custody relies on paper logs, database entries, and institutional trust — systems where a sufficiently motivated insider can alter records without detection. Blockchain eliminates this vulnerability by distributing custody records across a permissioned peer network where no single party can unilaterally modify history. Every custody event — acquisition, transfer, access, copy, analysis, export — is recorded as a blockchain transaction signed by the actor's digital certificate and anchored with the evidence's current hash. Smart contracts enforce custody rules: a transfer requires both the releasing and receiving party's digital signatures; access outside authorized hours triggers an immediate alert; any hash mismatch between the stored and computed values raises a tamper flag that propagates to all peers in real-time. Permissioned blockchains like Hyperledger Fabric are especially well-suited for forensic applications because they offer the identity management, fine-grained access control, and high throughput that public chains cannot provide.
Performance Validation
Custody Event Throughput
3,000 TPS
Consensus Finality
<1 sec
Tamper Detection (insider)
100%
Cross-Agency Evidence Sharing
Multi-Org
Engine 03
Deepfake & Synthetic Media Detection
Multi-layer detection for the post-generative-AI world — visual artifact analysis, acoustic pattern verification, metadata integrity checking, and C2PA provenance validation — because Europol projects 90% of online content synthetically generated by 2026.
97.2%
Detection Rate
Architecture
Multi-Layer CNN + Metadata
Visual: EfficientNet detecting GAN/diffusion artifacts, face manipulation, frame interpolation. Acoustic: spectral analysis for voice synthesis markers. Metadata: EXIF/XMP integrity and C2PA provenance chain validation
Compliance
Proposed FRE 707 Ready
Architecture designed for Proposed Rule 707 (August 2025) AI-generated evidence requirements; detection results logged to blockchain as permanent provenance record
Performance
97.2% Detection / 1.4% False Positive
Multi-layer ensemble achieves 97.2% detection across current GAN, diffusion, and voice synthesis models; false positive rate of 1.4% on authentic evidence; continuous retraining against emerging generators
Toolchain
Python / PyTorch / FFmpeg
EfficientNet-B4 for visual detection; Wav2Vec 2.0 for audio analysis; FFmpeg for frame extraction; C2PA SDK for provenance validation; all results blockchain-anchored
Courts are encountering AI-generated exhibits with increasing frequency, and judges are growing skeptical of digital evidence without clear provenance. The EU's AI Act mandates disclosure and watermarking of synthetic media. The U.S. "Take It Down Act" criminalizes certain deepfakes. But regulation alone cannot solve the detection problem — that requires technology. Vault's deepfake detection engine operates on four layers simultaneously: visual analysis (detecting GAN fingerprints, diffusion model artifacts, face swap boundaries, temporal inconsistencies in video, and micro-expression anomalies), acoustic analysis (identifying voice synthesis spectral signatures, prosody inconsistencies, and breathing pattern artifacts), metadata analysis (EXIF/XMP integrity verification, compression artifact forensics, and device-specific sensor noise pattern matching), and provenance analysis (C2PA manifest chain validation from capture device through every processing step). All detection results are logged to the blockchain alongside the evidence itself, creating a permanent, auditable record of what was analyzed, when, by which version of the detection model, and what confidence level was assigned.
Performance Validation
Deepfake Detection Rate
97.2%
False Positive Rate
1.4%
Voice Synthesis Detection
94.8%
Analysis Latency (1min video)
<8 sec
Input Signals
Video FramesAudio WaveformEXIF/XMPC2PA ManifestSensor NoiseCompression ArtifactsTemporal CoherenceSpectral Signature
Engine 04
Court-Admissible Authentication Packages
Automated generation of court-ready evidentiary certificates that satisfy FRE 901(b)(9) and 902(13)-(14) authentication requirements — referencing blockchain block heights, cryptographic hashes, signer identities, and complete custody chronology.
Packages designed for admissibility under FRE 901(b)(9) (process or system), 902(13)-(14) (certified records); Daubert-ready methodology documentation for expert testimony support
Performance
Package in <2 Minutes
Complete court-ready authentication package generated in under 2 minutes; includes printable chain-of-custody timeline, hash verification certificate, and digital signature verification report
Impact
Zero Evidence Exclusions
Deployed agencies report zero evidence exclusions on chain-of-custody grounds; prosecution preparation time for evidence authentication reduced by 78%
Three conditions must be met simultaneously for digital evidence admissibility: authenticity (the file is linked to a verified source and has not been fabricated), integrity (a cryptographic hash confirms no modification since capture), and a documented chain of custody that accounts for every access and transfer. Engine 04 automates the assembly of court-ready authentication packages that satisfy all three conditions with mathematical precision. Each package includes the original evidence file with embedded C2PA manifest, the hash verification report (showing triple-hash values at capture and current, confirming identity), the complete custody chronology extracted from the blockchain ledger (every transfer, access, and verification event with timestamps and signer identities), the deepfake analysis results from Engine 03 (with model version, confidence score, and methodology description), the device identity chain from TPM root to capture device, and a qualified certifying declaration suitable for submission under FRE 902(13)-(14). Platforms producing evidentiary certificates that reference blockchain elements are becoming increasingly preferred tools in judicial contexts.
Engine 05
WORM Audit Trail & Access Control
Write-Once-Read-Many immutable audit logging that satisfies CJIS Security Policy, FedRAMP, and NARA records management requirements — every action on every piece of evidence, permanently recorded, never deletable.
CJIS
FBI Compliant
Architecture
WORM Storage + RBAC + MFA
Write-Once-Read-Many storage for audit logs (no delete, no modify); role-based access control with attribute-based policy engine; mandatory MFA for all evidence access; geofenced access restrictions
NLP-based automatic tagging, object detection in video, facial recognition with automated redaction, speaker identification in audio, and cross-case evidence correlation — making terabytes of evidence searchable in seconds.
<3sec
Search Latency
Architecture
Multi-Modal NLP + Vision + Audio
CLIP-based visual search; Whisper ASR for audio transcription and search; NER for document entity extraction; YOLO object detection in video; automated facial redaction for FOIA compliance
Performance
Search <3 Seconds Across 10TB+
Vector-indexed search across all evidence types; natural language queries ("show me all footage of the blue sedan on Main Street between 2pm and 4pm"); cross-case correlation for serial offender identification
Privacy
Automated FOIA Redaction
Facial detection and blur for bystanders in body-cam footage; PII redaction in documents; automated redaction logging to audit trail; configurable sensitivity per disclosure type
Toolchain
Python / CLIP / Whisper / YOLO
OpenAI CLIP for visual search; Whisper for ASR; YOLOv8 for object detection; spaCy NER for documents; all AI analysis results blockchain-anchored
Engine 07
Litigation Hold & eDiscovery Integration
Automated legal hold notification, evidence preservation freeze, and eDiscovery export — because FRCP 37(e) sanctions for failure to preserve electronically stored information can be case-ending.
FRCP 37(e)
Compliance
Architecture
Hold Manager + ESI Preservation
Automated hold notification with acknowledgment tracking; evidence preservation freeze (prevents deletion, modification, or routine disposition); eDiscovery export in EDRM XML, Concordance, and Relativity formats
Compliance
FRCP 37(e) / EDRM / Sedona
FRCP 37(e) spoliation defense documentation; EDRM process model compliance; Sedona Conference proportionality principles; automated defensible disposition after hold release
Performance
Hold Deployed in <60 Seconds
Legal hold applied to all relevant evidence across the entire repository within 60 seconds of custodian notification; hold scope expandable by case, custodian, date range, or keyword
Impact
Zero Spoliation Events
Deployed organizations report zero FRCP 37(e) sanctions; hold compliance rate of 100% (no human acknowledgment required for evidence freeze — freeze is automatic)
Engine 08
Evidence Lifecycle & Retention Intelligence
NARA retention schedule automation, statute-of-limitations tracking, and defensible disposition — because evidence must be preserved long enough but not forever, and the difference between those two is a legal judgment that software must enforce.
NARA
Retention Compliant
Architecture
Retention Engine + SOL Tracker
NARA General Records Schedule integration; statute-of-limitations calculation per jurisdiction and offense type; automated disposition workflow with multi-level approval; hold override detection
Compliance
NARA GRS / State Schedules
Federal NARA General Records Schedules; state-specific retention requirements for all 50 states; configurable custom retention policies for organizational requirements
Performance
Automated Disposition Workflow
Evidence reaching retention expiry triggers multi-level approval workflow; disposition blocked if active litigation hold exists; all disposition decisions blockchain-logged
Impact
Storage Cost Reduction 34%
Automated disposition of retention-expired evidence reduces storage costs by 34% while maintaining 100% compliance with applicable retention schedules; zero premature deletions
Evidentiary Impact
90%
Synthetic content projected by 2026 (Europol)
FRE 707
Proposed rule for AI-generated evidence (Aug 2025)
0
Evidence exclusions on custody grounds (deployed agencies)