4. Traceability Architectures

Alternative Detection and Provenance Systems for the AI Era

Hypothetical Framework — Prepared by Adservio Innovation Lab Olivier Vitrac (former Research Director, Université Paris-Saclay) For internal discussion — November 2025

Disclaimer

This memo explores next-generation traceability architectures that could complement or replace traditional fingerprinting for music rights detection. Proposals range from enhanced signal processing (phase-domain signatures, perceptual hashing) to distributed ledger technologies (blockchain registries) and cryptographic watermarking. All concepts are presented as exploratory frameworks requiring validation through pilot programs.

4.1 Design Requirements for AI-Resilient Traceability

4.1.1 Core Challenges (Recap from Memo 3)

4.1.2 Proposed Architecture Layers

4.2 Layer 1: Cryptographic Watermarking

4.2.1 Concept

Embed imperceptible digital signature directly into audio waveform at creation/publication time:

• Survives lossy compression (MP3, AAC)

• Survives pitch shift, time stretch (within limits)

• Cannot be removed without perceptual degradation

4.2.2 Technical Approach: Spread-Spectrum Watermarking

Principle: Modulate low-amplitude pseudo-random noise sequence into audio signal

Mathematical Formulation:

where:

• $s_{\text{original}}(t)$$s_{\text{original}}(t)$: original audio signal

• $w(t)$$w(t)$: pseudo-random watermark sequence (generated from cryptographic key)

• $\alpha$$\alpha$: embedding strength (typically -40 to -60 dB below signal level)

Detection: Correlate suspected audio with $w(t)$$w(t)$ using secret key; high correlation confirms presence

4.2.3 Robustness Analysis

4.2.4 Practical Example: Vivendi Pilot

Scenario: UMG embeds watermarks in all new releases starting 2026

Implementation:

1. Embedding: During mastering, add watermark using proprietary key (e.g., per-album or per-track key)

2. Registry: Store hash of watermark key + ISRC on private blockchain (Layer 3)

3. Detection: Platforms (YouTube, TikTok) run watermark extraction on user uploads

4. Reporting: Detected watermarks trigger automatic reports to SACEM

Cost:

• Embedding: ~€1–5 per track (one-time, integrated into mastering workflow)

• Detection: Platform infrastructure (~€1M setup + €0.10 per 1000 detections)

• Registry: ~€50k/year for private blockchain (see Section 4.4)

Benefit:

• Survives most AI remixes (except extreme degradation)

• Non-repudiable proof of origin (cryptographic)

• Complements existing fingerprinting (dual-layer defense)

4.3 Layer 2: Enhanced Signal Detection

4.3.1 Phase-Domain Signatures

Motivation: Magnitude-only fingerprints ignore phase information; phase is sensitive to transformations but can be stabilized

Approach: Use instantaneous frequency (rate of phase change) and group delay (frequency-dependent time delay)

Mathematical Basis:

• Instantaneous frequency: $f_i(t)=\frac{1}{2\pi }\frac{d\varphi (t)}{dt}$$f_i(t) = \frac{1}{2\pi} \frac{d\phi(t)}{dt}$

• Group delay: $\tau (\omega )=-\frac{d\varphi (\omega )}{d\omega }$$\tau(\omega) = -\frac{d\phi(\omega)}{d\omega}$

Advantage: Pitch shift and time stretch alter $f_i(t)$$f_i(t)$ in predictable ways → can be normalized

Implementation:

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# Pseudocode for phase-based fingerprint
import librosa
import numpy as np

def phase_fingerprint(audio, sr=22050):
    # Compute STFT
    D = librosa.stft(audio)
    magnitude, phase = np.abs(D), np.angle(D)

    # Compute instantaneous frequency
    phase_diff = np.diff(phase, axis=1)
    inst_freq = phase_diff / (2 * np.pi)

    # Identify stable phase regions (peaks)
    stable_regions = detect_peaks(inst_freq)

    # Generate hash from phase constellation
    fingerprint = hash_constellation(stable_regions)
    return fingerprint

Robustness (hypothetical):

• Pitch shift: Normalizable (detect shift factor, correct before hashing)

• Time stretch: Partially robust (phase coherence preserved)

• Compression: Moderate (phase is sensitive to quantization noise)

4.3.2 Perceptual Hashing (Deep Learning)

Motivation: Human perception is invariant to many transformations (pitch shift within octave, slight tempo change) → train neural network to mimic this

Approach:

1. Train autoencoder on large music dataset

2. Use latent representation (compressed embedding) as "perceptual fingerprint"

3. Similar-sounding tracks cluster in latent space

Architecture (simplified):

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Input: Audio spectrogram (128 mel bins × 1000 frames)
   ↓
Encoder: Conv layers → 128-dim latent vector
   ↓
Decoder: Deconv layers → reconstruct spectrogram
   ↓
Loss: Reconstruction + perceptual loss (STFT distance)

Advantage: Learns to ignore irrelevant variations (pitch, tempo) while preserving identity

Challenge: Requires massive training set + continuous retraining as AI remixing techniques evolve

Deployment: Could be integrated into Content ID as "second-stage filter" (broad match → perceptual verification)

4.3.3 Symbolic Melody Matching

Motivation: If acoustic signal is destroyed, fall back to symbolic representation (melody as sequence of notes)

Approach:

1. Use AI transcription (e.g., Google's MT3, Spotify's Basic Pitch) to extract melody

2. Convert to pitch interval sequence: [+2, -1, +3, +1, ...] (semitone deltas)

3. Compare against SACEM's composition database (if available)

Robustness:

• Pitch shift: Interval sequence unchanged (transposition-invariant)

• Time stretch: Irrelevant (only pitch intervals matter)

• Harmonization changes: Moderate impact (melody may be obscured)

Limitation: Requires SACEM to maintain symbolic scores for compositions (not universally available)

4.4 Layer 3: Blockchain-Anchored Registry

4.4.1 Motivation

• Current SACEM registry is centralized, opaque to rights holders

• Blockchain provides tamper-proof audit trail and cryptographic proof of registration time

Clarification: This is not about "Web3 monetization" or NFTs. It's about using distributed ledger as forensic infrastructure.

4.4.2 Hybrid Architecture: SACEM + Private Blockchain

Design:

1. SACEM maintains authoritative registry (legal/administrative continuity)

2. Blockchain stores cryptographic hashes of works + timestamps

3. Rights holders can independently verify registrations (trustless audit)

4.4.3 Technical Implementation

Blockchain Choice:

• Not Ethereum (too expensive, public, slow)

• Private consortium chain (e.g., Hyperledger Fabric, Polygon Edge)

  • Validators: SACEM, major publishers (UMG, Sony, Warner), platforms (Spotify, YouTube)

  • Throughput: 1000+ transactions/sec

  • Cost: ~€0.0001 per registration

Data Structure:

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{
  "block_number": 12345,
  "timestamp": "2025-11-05T14:32:00Z",
  "transactions": [
    {
      "tx_id": "0xabc123...",
      "type": "work_registration",
      "hash": "sha256(ISRC + audio_fingerprint + watermark_key)",
      "metadata": {
        "ISRC": "FRZ123456789",
        "ISWC": "T-123.456.789-0",
        "registrant": "Universal Music Publishing"
      }
    }
  ]
}

Verification Flow:

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# Pseudocode: Platform verifies work detected in upload
def verify_work(detected_isrc, detected_audio):
    # Query SACEM database
    sacem_record = sacem_api.lookup(detected_isrc)

    # Query blockchain for hash
    blockchain_record = blockchain_api.get_block_by_isrc(detected_isrc)

    # Compute hash of detected audio
    computed_hash = sha256(detected_isrc + extract_fingerprint(detected_audio))

    # Verify integrity
    if blockchain_record['hash'] == computed_hash:
        return {"verified": True, "timestamp": blockchain_record['timestamp']}
    else:
        return {"verified": False, "reason": "Hash mismatch"}

4.4.4 Practical Example: Dispute Resolution

Scenario: Two parties claim ownership of same melody

Classical Process:

• SACEM reviews submissions → slow (months)

• Relies on paper records, email timestamps (disputable)

Blockchain-Enhanced Process:

1. Query blockchain for earliest registration of melody hash

2. Cryptographic timestamp is non-repudiable

3. Dispute resolved in days (not months)

4.4.5 Cost-Benefit Analysis (Vivendi Pilot)

Setup Cost:

• Blockchain infrastructure: €200k (one-time)

• Integration with SACEM systems: €500k (one-time)

• Annual maintenance: €50k

Operational Cost:

• €0.0001 per work registration

• 100k new works/year → €10/year in transaction fees

Benefit:

• Faster dispute resolution (save €100k+/year in legal fees)

• Enhanced trust with artists/publishers (non-tangible)

• Compliance with future EU AI Act transparency requirements

4.5 Layer 4: AI Training Audits & Provenance

4.5.1 The Training Data Problem

Current State:

• AI models (Suno, Udio, MusicLM) trained on massive datasets

• No disclosure of which works were included

• Rights holders cannot prove their works were used

Proposed Solution: Require AI companies to:

1. Register training datasets with hash manifests

2. Anchor manifests on blockchain (tamper-proof)

3. Pay "training royalties" to rights holders (via SACEM or direct)

4.5.2 Technical Approach: Dataset Fingerprinting

Method:

1. AI company computes fingerprint (or watermark hash) for each training track

2. Generates Merkle tree of hashes

3. Publishes Merkle root on blockchain

Verification:

• Rights holder provides ISRC → AI company provides Merkle proof → verified if proof valid

Merkle Tree Structure:

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                Root Hash
               /          \
        Hash(A+B)       Hash(C+D)
        /      \         /      \
   Hash(A)  Hash(B)  Hash(C)  Hash(D)
     |        |        |        |
  Track_1  Track_2  Track_3  Track_4

Advantage: Compact proof (log(N) size), tamper-evident

4.5.3 Regulatory Pathway: EU AI Act Compliance

Current EU AI Act (2024):

• High-risk AI systems must document training data

• Music generation models are not classified as high-risk (as of 2024)

Proposed Amendment (Vivendi could advocate):

• Classify music generation as "high-risk to IP rights"

• Mandate dataset disclosure + training royalty payments

• Enforcement: Platforms must verify AI-generated content comes from compliant models

4.6 Layer 5: Economic Automation (Smart Contracts)

4.6.1 Concept

Use blockchain smart contracts to automate royalty splits and payments:

• Platform detects usage → triggers smart contract

• Contract automatically splits payment (e.g., 50% publisher, 30% songwriter, 20% performer)

• Settlement in real-time (not quarterly as with SACEM)

4.6.2 Simplified Smart Contract (Pseudocode)

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// Solidity-style pseudocode (simplified)
contract MusicRoyalty {
    struct Work {
        string ISRC;
        address payable publisher;
        address payable songwriter;
        uint8 publisherShare;  // percentage
        uint8 songwriterShare;
    }

    mapping(string => Work) public works;

    function registerWork(
        string memory ISRC,
        address payable publisher,
        address payable songwriter,
        uint8 pubShare,
        uint8 songShare
    ) public {
        works[ISRC] = Work(ISRC, publisher, songwriter, pubShare, songShare);
    }

    function reportUsage(string memory ISRC) public payable {
        Work memory work = works[ISRC];
        uint256 pubAmount = msg.value * work.publisherShare / 100;
        uint256 songAmount = msg.value * work.songwriterShare / 100;

        work.publisher.transfer(pubAmount);
        work.songwriter.transfer(songAmount);
    }
}

4.6.3 Integration with SACEM

Hybrid Model:

• SACEM retains legal authority (contracts, disputes)

• Smart contract handles settlement only (not rights management)

• Monthly reconciliation between on-chain payments and SACEM records

4.6.4 Advantages

• Speed: Real-time payments (vs. quarterly SACEM distributions)

• Transparency: All payments auditable on-chain

• Reduced friction: No intermediary holding funds

4.6.5 Challenges

• Volatility: If settled in cryptocurrency (avoid by using stablecoins or fiat-pegged tokens)

• Gas fees: Solved by using Layer 2 or private chain (€0.0001/transaction)

• Legal recognition: Smart contract payouts must be recognized by tax authorities (ongoing policy work)

4.7 Integrated System Architecture

4.7.1 Full Stack Overview

4.7.2 Data Flow (End-to-End)

4.8 Comparative Analysis: Approaches vs. Requirements

*Watermarking detects generative AI only if training data was watermarked †Cannot watermark existing releases (requires re-mastering) ‡Cannot audit past training datasets (but can enforce for future models)

4.9 Recommended Hybrid Strategy for Vivendi

4.9.1 Short-Term (2025–2026)

Goal: Improve detection of AI remixes within existing infrastructure

Actions:

1. Pilot watermarking on new UMG releases (select high-value artists)

2. Partner with platforms to integrate watermark detection alongside Content ID

3. Establish private blockchain registry (Vivendi + SACEM + major publishers)

Investment: ~€1M (setup) + €200k/year (operations)

4.9.2 Medium-Term (2026–2028)

Goal: Expand detection coverage and automate settlements

Actions:

1. Scale watermarking to 100% of new releases

2. Deploy phase-domain fingerprinting as second-stage filter on YouTube, TikTok

3. Launch smart contract pilot for real-time royalty splits

4. Advocate for EU AI Act amendment (training data transparency)

Investment: ~€5M (cumulative)

4.9.3 Long-Term (2028–2030)

Goal: Establish Vivendi as leader in AI-resilient IP protection

Actions:

1. Industry standard: Propose watermarking + blockchain as ISO/IEC standard for music traceability

2. AI training royalties: Secure regulatory mandate for training dataset disclosure + compensation

3. Cross-sector expansion: Apply model to video (Canal+), games (Vivendi Gaming)

Investment: ~€10M (cumulative)

4.10 Risk Analysis

4.10.1 Technical Risks

4.10.2 Economic Risks

4.10.3 Regulatory Risks

4.11 Summary: No Single Silver Bullet

Key Insights

1. Watermarking is most mature and deployable today

   • Protects new releases, but not back catalog

   • Survives most AI transformations (except extreme)

2. Phase-domain / perceptual hashing offers best robustness

   • But requires significant R&D and platform buy-in

3. Blockchain provides provenance and audit layer

   • Cheap, trustless, but doesn't detect by itself

4. AI training audits address generative synthesis

   • Requires regulatory mandate (not yet in place)

5. Optimal strategy is multi-layered

   • Combine watermarking (embedded defense) + enhanced detection (phase/perceptual) + blockchain (provenance) + policy advocacy (training royalties)

Strategic Recommendation for CTO

Vivendi should champion a "defense-in-depth" approach:

• Invest in watermarking (immediate protection)

• Partner on phase-domain R&D (medium-term)

• Lead blockchain consortium (establish standard)

• Advocate for AI training transparency (long-term policy win)

4.12 Next Steps

Memo 5 will synthesize findings into a strategic roadmap with:

• Discussion questions for CTO meeting

• Pilot program proposals

• Regulatory engagement strategy

• Success metrics and timeline

End of Memo 4 Prepared by Adservio Innovation Lab — Hypothetical Framework Contact: olivier.vitrac@adservio.fr