Aimbot Humanization

Aimbot humanization

Make aim assistance look like a skilled human, not a math function locked to the skull.

Perceptual model

• Humans overshoot/correct; imperfect tracking on strafe
• Reaction delay 120–280ms typical; not constant
• Mouse has accel/raw input quirks; camera has sensitivity curves
• Target switches cost time; not instant snap

Control stack

1. Target select: FOV cone, visibility/LOS, team/hp filters, priority scores
2. Pathing: angle delta → easing curve (ease-in-out, crit-damped spring, Bezier)
3. Noise: Ornstein–Uhlenbeck / band-limited noise on yaw/pitch
4. Deadzone: don't micro-twitch on tiny errors
5. Velocity match: track target velocity; lead for projectiles
6. Breaks: occasional intentional miss / recapture
7. Input device model: mouse counts vs controller stick acceleration

Parameters (tune per game)

• max FOV, max angular velocity, acceleration, smoothing window
• reaction delay distribution (log-normal)
• bone blend (head/chest) with context (range/weapon)
• hit-chance scheduler independent of raw lock

Anti-detection themes

• Avoid perfect bone lock every frame
• Avoid identical tick-aligned snaps
• Correlate with visible animation / ADS state
• Don't aim through freshly broken LOS without delay

Implementation sketch

each frame:
  if !should_engage(): release_soft(); return
  tgt = select_target()
  err = angle_to(tgt.aim_point + lead)
  err = apply_reaction_delay(err)
  delta = clamp(smooth(err) + noise(), max_rate)
  apply_mouse_or_view(delta)

Concrete parameterizations (shipped-tested shapes)

Reaction model

• Two-stage: detection (uniform 120-200ms) + motor onset (log-normal μ≈5.1 σ≈0.4, median 165ms). First engagement of a match slower (+15%); target-switch faster than fresh acquisition (60% of fresh reaction).
• Per-target memory: reacquisition of a recently-seen target uses 40-60% reaction — models expectation.

Smoothing curves ranked by realism

• Feed-forward: add target angular velocity term so tracking lags behind strafes by a small constant (30-60ms virtual latency) instead of locking.

Noise that survives spectral analysis

• OU process: dθ = -θ/τ dt + σ√dt·N(0,1) with τ≈80-120ms — band-limited, no white-spike signature. Tune σ per-axis (yaw 2-3× pitch: humans correct yaw harder).
• Micro-tremor: 8-12Hz small-amplitude (0.05-0.15°) additive sinusoid — matches physiological hand tremor bands.
• Correlate noise with error magnitude (bigger err → bigger overshoot variance); uncorrelated noise is itself a detector.

Overshoot/correction

• P(overshoot) ≈ 15-25% on fast flicks (>15°), near-zero on micro-corrections; overshoot magnitude 5-12% of flick angle, correction settle 1-2 counter-moves over 150-300ms.
• Miss scheduler: on low-visibility/high-motion shots, deliberately seed 3-8% miss offsets (first-bullet high on spray transfer is the human-tell you fake in reverse).

Detectors to stay under (what AC measures)

• Angular-velocity spectrogram: pure lock = single delta repeated; human = broad spectrum with 1/f falloff — OU noise provides this.
• Crosshair-bone coincidence rate: cap total time-on-bone < 65% even when perfect tracking possible; blend bone target (head/neck/chest per-shot sampling).
• Reaction-time floor: never fire < 120ms after LOS gain; jitter never < 40ms from same stimulus twice.
• Tick alignment: apply aim on render frames (not net ticks) with ±half-frame jitter — tick-aligned actions = bot signature.

Failsafe interlocks

• Disable on: death/spectate, scoreboard/chat/menu focus, loading screens (view matrix invalid), replay/editor capture active.
• Soft-release on target death: keep tracking velocity for 80-150ms then decay — instant release at exact death tick is a tell.

Pair with

aimbot-triggerbot, game-internals, game-hacking.