Discuss R6 Siege AI Aimbot — Driver Hijacking & Model Training

[byte_corvus]

Thinking about ditching memory-based internals for a vision-based approach in Siege? BattlEye hasn't been kind to sloppy memory R/W lately, so moving to an external AI-based stack is a logical step if you want to keep your main account from being flagged by integrity checks. The theory is simple: capture frames, run inference, and move the mouse via a driver—avoiding the whole memory-patching headache entirely.

The Tech Stack: Python vs. C++
If you're just starting, Python with OpenCV and PyTorch is the standard for prototyping. However, if you're chasing that sub-10ms latency on an RTX 3060, you'll eventually want to move the heavy lifting to C++ using TensorRT. For an R6 environment where every millisecond counts—especially during a fast peek—C++ will give you the edge in frame acquisition and inference speed.

Model Selection and Performance
For a mid-high end setup like a Ryzen 5 7600x, you should be looking at:

1. YOLOv8n (Nano) or YOLOv10n: These are optimized for speed. On a 3060, you can easily hit 3-5ms inference times.
2. Resolution: Don't try to process 1080p. Crop to a center FOV (e.g., 320x320 or 416x416) to keep the pipeline lean.
3. TensorRT: This is non-negotiable. Converting your .pt model to an engine file is the only way to get real-time performance.

Input Handling: The Driver Problem
Windows API (mouse_event / SendInput) is an instant flag for BattlEye. You have two main routes here:

1. Hijacking Signed Drivers: Using older, vulnerable versions of Logitech G Hub or Razer drivers. It's a classic "paste" move, but it still works if you handle the IOCTLs correctly.
2. Custom Mapped Driver: Writing a simple KMDF driver and mapping it via a vulnerable driver (like the Intel or Capcom ones). This is the gold standard for staying under the radar.

Spoiler: Dataset and Training Logic

Training your own model is the only way to avoid the "AI delay" and weird snapping issues.
- Aim for a dataset of 3,000 to 5,000 images minimum.
- Focus on R6-specific assets: headgear, body outlines against different map textures (Club House, Oregon), and even through destruction holes.
- Collection method: Write a script to save frames when you hold your aim key during regular gameplay. You'll then need to manually label them (tools like CVAT or LabelImg).

The Reality Check
AI isn't a magic bullet. BattlEye can still look for inhuman mouse movement patterns (Aimbot Smoothing is your best friend here) and anomalous input origins. If you don't randomize your delays and smooth your curves, you'll find yourself in a ban wave regardless of how "undetectable" your vision method is.

Anyone here experimenting with the new YOLOv10 architectures for R6 yet, or are we staying on v8 for stability?