Entropy Budgeting: How to Distribute "Uniqueness" Between Canvas, WebGL, and AudioContext

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Entropy balancing strategy to achieve optimal likelihood level

Introduction: The Paradox of Uniqueness​

You set up a profile in Dolphin Anty:
Canvas noise — 100%,
WebGL renderer — rare NVIDIA RTX,
AudioContext — maximum entropy.

You think, "The more unique, the better".
But it's precisely this hyper-uniqueness that instantly reveals you as a fake.

Because real users aren't unique. They're average. Their fingerprint is a balance between variability and normality.

In this article, we'll look at how to distribute entropy between Canvas, WebGL, and AudioContext to achieve an optimal level of verisimilitude —not too unique, but not cookie-cutter either.

Part 1: What is Entropy Budgeting?​

Technical definition​

Entropy Budgeting is a strategy for distributing "uniqueness" among fingerprint components so that:

• The total entropy remained within the real distribution,
• No component stood out as a statistical anomaly.

Key insight:
Fraud engines aren't looking for the "perfect" profile. They're looking for a "plausible" one.

Part 2: Entropy by Components​

Real user statistics (2026)​

Component	Average entropy	Normal range	Risk when exceeded
Canvas	Pages 10–14	Pages 8–16	High (too much noise)
WebGL	Pages 12–18	10–20 pages	Average (rare GPU)
AudioContext	Pages 6–10	Pages 5–12	Low (less tracked)

Example of anomaly:

• Canvas: 20 bit (too many fonts/noise),
• WebGL: NVIDIA RTX 4090 (0.3% of users),
• AudioContext: 15 bits (maximum entropy).
  → Fraud Score = 95+

Part 3: Principles of Entropy Balancing​

Rule 1: A weak component compensates for a strong one​

• If you are using a rare GPU (high WebGL entropy),
• Then Canvas and AudioContext should be as standard as possible.

Example:

• WebGL: ANGLE (Intel, D3D11) (low entropy),
• Canvas: 65% noise (12 bits),
• AudioContext: 8 bit.
  → Perfect balance.

Rule 2: Total entropy ≤ 35 bits​

• The sum of the entropy of all components must not exceed 35 bits,
• This corresponds to 1 in 34 billion - fairly unique, but not anomalous.

Calculation:

• Canvas: 12 pages,
• WebGL: 15 bit,
• AudioContext: 8 bit,
• Total: 35 bits → safe.

Rule 3: Canvas and WebGL Prioritize​

• AudioContext is less tracked,
• The main focus is on Canvas + WebGL consistency.

Part 4: How to Set Up Entropy Budgeting in Dolphin Anti​

Step-by-step setup​

Step 1: Select a Base GPU

• Intel UHD 620 (70% market share) → low entropy WebGL.

Step 2: Set up the Canvas

• Noise: 65% → ~12 bits of entropy,
• Fonts: 25 system → avoid custom fonts.

Step 3: Set up AudioContext

• Sample Rate: 44100 Hz (standard),
• Channel Count: 2 (stereo),
• Latency: 0.01–0.02 sec.

Step 4: Check the total entropy

• Use amiunique.org:
  • Canvas entropy: 10–14 bits,
  • WebGL entropy: 12–16 bits,
  • Audio entropy: 6–10 bits.

Result:
The profile will look like a real user of a laptop with Intel GPU.

Part 5: Why Most Carders Fail​

Common Mistakes​

Error	Consequence
Maximum entropy in everything	Looks like a test profile → high-risk score
Inconsistency of components	Intel GPU + 100% Canvas noise → anomaly
Ignoring the total entropy	Amount > 40 bits → 1 in 1 trillion → flag

Field data (2026):
82% of failures are due to profile hyperuniqueness.

Part 6: Practical Examples​

Example 1: Secure Profile​

• GPU: Intel UHD 620,
• Canvas: 65% noise, 25 fonts,
• AudioContext: 44100 Hz, 2 channels,
• Total entropy: 32 bits → successful.

Example 2: Anomalous Profile​

• GPU: NVIDIA RTX 4090,
• Canvas: 100% noise, 200 fonts,
• AudioContext: 96000 Hz, 8 channels,
• Total entropy: 58 bits → instant ban.

Conclusion: Uniqueness is the enemy of credibility​

Fraud engines don't look for the "most unique" profile. They look for the most credible one.

And credibility is a balance, not maximalism.

Final thought:
True camouflage isn't about standing out, it's about blending in.
Because in the world of fingerprinting, average is golden.

Stay balanced. Stay statistically normal.
And remember: in the world of fraud, entropy is a budget, not a goal.