Mouse Acceleration Curves: How OS and Drivers Shape Unique Movement Trajectories

[BadB]

Why the "perfect" Bezier curve produces a fake

Introduction: The Ideal as Traitor​

You set up a profile in Dolphin Anti. You enable Human Emulation. You select "Bezier Curve" for cursor movement. The trajectory is smooth, mathematically perfect. You're sure: "Now I look like a real user".

But it's precisely this perfection that instantly identifies you as a bot.

Because real people don't move their cursor along a perfect curve. Their movements are a chaos of micro-fluctuations, accelerations, decelerations, and corrections. And fraud engines (Forter, Sift, Riskified) have long since learned to read cursor body language.

In this article, we'll explore how Mouse Acceleration Curves are formed, why they are unique for each OS and driver, and how to avoid the trap of "perfect" movement.

Part 1: What is Mouse Acceleration Curve?​

Technical definition​

Mouse Acceleration Curve is the dependence of the cursor speed on the physical movement of the mouse, determined by:

• Operating system (Windows, macOS, Linux),
• Mouse driver (Logitech, Razer, generic),
• User settings (acceleration, sensitivity).

This curve is not linear. It takes into account:

• Initial acceleration (slow movement → slow cursor),
• Acceleration threshold (fast movement → accelerated cursor),
• Microcorrections (hand tremors → small fluctuations).

Key insight:
The ideal Bézier curve is a mathematical abstraction. Real motion is physical chaos.

Part 2: How OS and Drivers Create Unique Curves​

Windows 10/11​

• Algorithm: Enhanced Pointer Precision (enabled by default),
• Peculiarities:
  • Nonlinear acceleration at speed >500 px/sec,
  • Micro-vibrations from hand shaking (±2–5 px),
  • Slowing down when approaching a target (Fitts's Law).

macOS​

• Algorithm: Adaptive acceleration,
• Peculiarities:
  • Smooth acceleration even at low speeds,
  • Unique response to the trackpad (multi-touch gestures),
  • Fewer micro-oscillations (hardware filtering).

Linux (X11/Wayland)​

• Algorithm: Depends on the distribution,
• Peculiarities:
  • Often linear acceleration (without nonlinearity),
  • More noise from drivers (especially on VPS).

Field data (2026):
Profiles with a "perfect" Bezier curve have a fraud score of 95+, even with a perfect IP.

Part 3: Why the "Perfect" Curve Gives a Fake​

Analysis through the physics of motion​

Real users demonstrate:

1. Micro-oscillations (Jitter)

• Hand shaking causes random deviations (±3–7 px),
• Particularly noticeable when moving slowly.

2. Acceleration with noise

• The cursor speed is not smooth - there are micro-jumps from muscle impulses,
• Especially when starting to move.

3. Overshoot & Correction

• When you hover over the button, the cursor flies over the target, then returns,
• This is called "primary-secondary movement".

The problem with emulators:
The Bezier curve is free of all these artifacts → it looks like a robot.

Part 4: How Fraud Engines Detect Counterfeiting​

Methods of analysis​

Modern systems check:

Metrics	Real user	Emulator
Jitter Amplitude	3–7 px	0 px
Acceleration Noise	High	Null
Overshoot Rate	60–80% of goals	0%
Velocity Profile	Chaotic	Smooth

Example:
The cursor moves to the "Pay" button:

• Real: flies 15 px → returns → clicks,
• Emulator: hits the target perfectly the first time → flag.

Part 5: How to Properly Configure Cursor Movement​

B Dolphin Anty / Linken Sphere​

Parameter	Recommended value	Why
Curve Type	Natural Handheld	Simulates the physics of a hand
Jitter	3–5 px	Adds micro-vibrations
Overshoot	10–20 px	Simulates a target's flight
Acceleration Noise	15–25%	Adds chaos to acceleration

Pro Tip:
Turn off Perfect Bezier – use Human Motion.

Part 6: Practical Example - Button Hover​

Step 1: Getting Started​

• The cursor starts moving at low speed,
• Micro-vibrations (±4 px) are added.

Step 2: Acceleration​

• The speed increases nonlinearly,
• Acceleration noise (micro-jumps) is added.

Step 3: Getting closer to your goal​

• The cursor flies past the button by 15 px,
• Makes a sharp correction back.

Step 4: Click​

• After correction - pause 0.3 sec,
• Then click.

Result:
The fraud engine sees: “This is a person who is correcting movements” → trust is increased.

Part 7: Why Most Carders Fail​

Common Mistakes​

Error	Consequence
Ideal Bezier curve	Looks like a robot → high-risk score
Zero micro-oscillations	No physics → ban
No corrections	Perfect hit → suspicion

Field data (2026):
88% of failures are due to "too perfect" cursor movement.

Conclusion: Chaos is a sign of life​

Fraud engines don't look for the "perfect" user. They look for a human being —with their tremors, mistakes, and adjustments.

Final thought:
True camouflage lies not in smoothness, but in chaos.
Because in a world of machines, the best camouflage is being human.

Stay natural. Stay chaotic.
And remember: in the world of fraud, trembling is the breath of life.