Deep Learning Anti-Cheat For CSGO

Use Python Version 3.9.11

Input the directory with your .dem files and the model outputs predictions for every shot during the game.

from DLAC import Model

model = Model("C:\\path\\to\\demo\\directory\\")
model.predict_to_terminal(threshold=0.90)

from DLAC import Model

model = Model("C:\\path\\to\\demo\\directory\\")
model.predict_to_csv(threshold=0.90, out_file'example.csv')

Installation

Windows should be as easy as:

pip install DLAC

Linux users will need to build the .so file. This requres GO.

git clone https://github.com/LaihoE/DLAC  
cd DLAC
python3 setup.py install
cd DLAC
go build -o parser.so -buildmode=c-shared

You can choose between a bigger and a smaller model

from DLAC import Model

model = Model("./path_to_demos/", model_type='big')
model.predict_to_terminal(threshold=0.99)   # 0.99 is recommended with the bigger model

The bigger model is slower with slightly better accuracy

Other ways to output predictions
model.predict_to_csv()
model.predict_to_list()

Example output from one shot

Name, Confidence of cheating, SteamId, File
PeskyCheater22, 0.9601634, 123456789, exampledemo.dem

Special thank you to

Demoinfocs-golang is the underlying parser used for parsing the demos, found at:
https://github.com/markus-wa/demoinfocs-golang.

87andrewh has written the majority of the specific parser used, found at: https://github.com/87andrewh/DeepAimDetector/blob/master/parser/to_csv.go