3. Detection Mechanisms & Limits

Technical Analysis of Current Rights Detection and AI-Induced Failure Modes

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

Disclaimer

This memo provides a technical analysis of music rights detection systems, focusing on acoustic fingerprinting, metadata propagation, and their failure modes under AI transformation. The analysis synthesizes publicly available research on Content ID, SACEM's detection mechanisms, and signal processing techniques. Specific implementation details of proprietary systems remain inferential.

3.1 Current Detection Paradigm: Content ID and Acoustic Fingerprinting

3.1.1 How Acoustic Fingerprinting Works

Acoustic fingerprinting (popularized by Shazam, adopted by YouTube Content ID, Spotify, etc.) operates on the following principles:

1. Spectral Analysis: Convert audio waveform to frequency domain (via Short-Time Fourier Transform, STFT)

2. Feature Extraction: Identify peaks in spectrogram (frequency vs. time)

3. Hash Generation: Create compact "fingerprint" from peak constellation

4. Database Matching: Compare query fingerprint against reference database

5. Scoring: Return matches above threshold confidence

3.1.2 Key Technical Characteristics

3.1.3 Content ID Workflow (YouTube Example)

3.2 SACEM's Detection Mechanisms (Hypothetical Reconstruction)

SACEM does not operate its own fingerprinting infrastructure (unlike labels). Instead, it relies on:

3.2.1 Declarative Metadata (ISWC + Catalogs)

• ISWC (International Standard Musical Work Code): Unique ID for compositions

• Publishers submit metadata: title, composers, lyricists, ownership splits

• Platforms cross-reference metadata against SACEM's catalog

Strength: Works when metadata is intact (e.g., official Spotify uploads)

Weakness: Fails when:

• Users rip files and strip ID3 tags

• AI generates synthetic tracks (no ISWC assigned)

• Platforms don't query SACEM database (non-EU platforms)

3.2.2 Platform-Mediated Detection

SACEM partners with platforms (YouTube, Spotify, Deezer) to receive usage reports:

• Platforms use their own fingerprinting (Content ID, Spotify's Echo Nest)

• Matches trigger reports to SACEM

• SACEM cross-references against ISWC registry

Strength: Scales to billions of streams

Weakness:

• SACEM depends on platform accuracy

• If platform misses a match (AI remix), SACEM never sees it

3.2.3 Radio/TV Monitoring

SACEM uses third-party monitoring services (e.g., BMAT, Yacast) to detect broadcasts:

• Audio surveillance of radio stations, TV channels

• Fingerprinting + metadata extraction

• Reports to SACEM for royalty distribution

Strength: Captures traditional broadcast (still ~25% of SACEM revenue)

Weakness: AI remixes on TikTok, YouTube Shorts bypass this entirely

3.3 Failure Modes: How AI Breaks Detection

3.3.1 Pitch Shifting (Frequency Domain Distortion)

Mechanism: AI (or manual tools) transpose audio by ±N semitones

Effect on Fingerprint:

• Spectral peaks shift proportionally: $f_{\text{new}}=f_{\text{original}}\times 2^{N/12}$$f_{\text{new}} = f_{\text{original}} \times 2^{N/12}$

• Hash constellation no longer matches reference

• Content ID fails if $|N|>1$$|N| > 1$ semitone (typical threshold)

Example:

• Original track: melody centered at 440 Hz (A4)

• Pitch-shifted +3 semitones: melody at 523 Hz (C5)

• Fingerprint hash: ABC123 → XYZ789 (no match)

Mitigation (Theoretical):

• Use pitch-invariant features (e.g., chroma vectors, which collapse octaves)

• Requires reprocessing entire reference database (expensive)

3.3.2 Time Stretching (Temporal Domain Distortion)

Mechanism: Change tempo without altering pitch (or vice versa)

Effect on Fingerprint:

• Peak timings shift: $t_{\text{new}}=t_{\text{original}}\times \alpha$$t_{\text{new}} = t_{\text{original}} \times \alpha$ (where $\alpha$$\alpha$ is stretch factor)

• Hash constellation geometry breaks

• Content ID fails if $|\alpha -1|>0.1$$|\alpha - 1| > 0.1$ (±10% tempo change)

Example:

• Original track: 120 BPM

• Time-stretched: 150 BPM (α = 1.25)

• Fingerprint: timing-based hashes become invalid

Mitigation (Theoretical):

• Use tempo-invariant features (e.g., beat-synchronized chroma)

• Computationally expensive, rarely deployed

3.3.3 Stem Separation + Recombination

Mechanism: AI separates audio into stems (vocals, drums, bass, melody), then recombines selectively

Effect on Fingerprint:

• Spectral balance completely altered

• Peak constellation differs (e.g., vocals emphasized, drums removed)

• Content ID may partial-match isolated stems, but full track fails

Example:

• Original track: full band mix

• AI remix: vocals from Track A + drums from Track B

• Fingerprint: no single reference match (hybrid signal)

3.3.4 Generative Synthesis (No Source Signal)

Mechanism: AI trained on corpus generates entirely new waveform

Effect on Fingerprint:

• Zero spectral overlap with any single training track

• Fingerprint is unique (by design)

• Content ID: no match possible

Implication: Even if the melody resembles a SACEM-registered composition, acoustic fingerprint won't detect it

Detection Approach: Would require symbolic music analysis (melody contour matching), not acoustic fingerprinting

3.4 Frequency Domain Limitations

3.4.1 Narrow-Band Focus

Most fingerprinting systems optimize for 300 Hz – 5 kHz (human speech + melody range):

• Rationale: Captures perceptually salient features, robust to noise

• Limitation: Ignores sub-bass (<100 Hz) and high-frequency transients (>10 kHz)

AI Exploit: Model could embed "signature" in sub-bass or ultrasonic range, invisible to Content ID

3.4.2 Phase Information Discarded

Standard fingerprints use magnitude spectrogram only (not phase):

• Rationale: Phase is unstable under noise, compression

• Limitation: Two signals with identical magnitude but different phase are perceptually different, yet have same fingerprint

AI Exploit: Phase manipulation (e.g., all-pass filters) can alter sound without changing fingerprint

3.5 Metadata Propagation: The Weak Link

3.5.1 How Metadata Is Embedded

Audio files carry metadata in:

• ID3 tags (MP3): title, artist, album, ISRC

• Vorbis comments (OGG, FLAC)

• iTunes metadata (M4A/AAC)

SACEM Reliance: Platforms extract ISRC → cross-reference with SACEM catalog

3.5.2 When Metadata Is Lost

3.5.3 Platform Behavior Variability

Implication: SACEM's coverage varies by platform, with TikTok/Instagram being high-risk zones for leakage

3.6 Cross-Platform Detection Gaps

3.6.1 The Multi-Hop Problem

User workflow:

1. Download track from Spotify (ISRC intact)

2. Remix using AI tool (ISRC stripped)

3. Upload to TikTok (no fingerprint match)

4. TikTok video re-uploaded to YouTube (now twice-removed from original)

Current Detection: Each platform operates independently → no cross-platform tracking

3.6.2 Jurisdictional Gaps

• SACEM's reach: Strong in EU, weak in US, Asia

• US platforms (TikTok, YouTube) may not query SACEM database

• Result: French composers lose royalties on US-based streams

3.7 Robustness Analysis: What Survives?

3.7.1 Transformations Ranked by Detection Survival

*Assumes no symbolic melody matching is deployed

3.7.2 Detection Success Rate vs. AI Adoption

Hypothetical projection (2025–2030):

If AI-mediated music grows from 5% (2025) to 30% (2030), SACEM's detection rate could drop from 90% to 60% (hypothetical).

3.8 Why Current Systems Cannot Easily Adapt

3.8.1 Architectural Constraints

• Backward compatibility: Changing fingerprint algorithm requires reprocessing billions of reference files

• Computational cost: Pitch/tempo-invariant features are 10–100× more expensive

• False positive risk: Broader matching increases collisions (unrelated tracks flagged)

3.8.2 Economic Incentives

• Platforms: Prefer under-detection (fewer royalty payouts, fewer DMCA disputes)

• SACEM: Wants over-detection, but lacks technical leverage over platforms

• AI startups: Actively benefit from under-detection (users prefer "royalty-free" tools)

3.8.3 Regulatory Lag

• EU Article 17: Requires "best efforts" but doesn't define technical standards

• AI Act: Focuses on high-risk applications (music generation is not classified as high-risk)

• Result: No legal mandate for platforms to upgrade detection systems

3.9 Alternative Detection Domains (Preview of Memo 4)

To overcome current limitations, next-generation systems could explore:

3.9.1 Phase-Domain Signatures

• Use phase coherence, group delay, or instantaneous frequency

• Survives magnitude-only transformations (pitch shift, EQ)

• Challenge: Computationally expensive, phase instability under compression

3.9.2 Perceptual Hashing

• Hash based on psychoacoustic model (what humans perceive, not raw signal)

• Survives lossy transformations (compression, pitch shift)

• Challenge: Requires deep learning models (not yet standardized)

3.9.3 Symbolic Melody Matching

• Extract melody contour (sequence of pitch intervals)

• Compare against SACEM's composition database

• Challenge: Requires music transcription (AI-powered, error-prone)

3.9.4 Blockchain-Anchored Watermarking

• Embed cryptographic signature in audio (imperceptible)

• Anchor hash on blockchain for tamper-proof provenance

• Challenge: Requires embedding at creation time (not retroactive)

3.10 Summary: Detection Is a Losing Battle Without New Paradigms

Key Findings

1. Current fingerprinting is narrow-band, magnitude-only, and fragile

   • Optimized for compression, not adversarial AI transformation

2. SACEM's hybrid model (declarative + automated) is under stress

   • Declarative fails when metadata is stripped

   • Automated fails when signals are transformed

3. Platforms have misaligned incentives

   • Under-detection reduces costs, legal exposure

   • No regulatory pressure to improve

4. AI transformations are designed to evade detection

   • Pitch shift, time stretch, stem swap all break fingerprints

   • Generative models produce zero-overlap signals

5. Revenue leakage is structural, not incidental

   • If 20% of music is AI-mediated by 2028, SACEM could lose €100–300M annually (hypothetical)

Strategic Implication for Vivendi

Incremental improvements to Content ID will not suffice. Vivendi must either:

• Invest in next-generation detection (phase-domain, perceptual hashing, symbolic matching)

• Advocate for regulatory mandates (AI Act amendments, Article 17 technical standards)

• Build parallel traceability infrastructure (blockchain registries, watermarking at creation)

3.11 Next Steps

The following memos will explore:

• Memo 4: Alternative traceability architectures (blockchain, watermarking, hybrid registries)

• Memo 5: Strategic roadmap and pilot concepts for Vivendi

End of Memo 3 Prepared by Adservio Innovation Lab — Hypothetical Framework Contact: olivier.vitrac@adservio.fr