Hello, Codeforces.
I've participated a few rounds and noticed that there are too many cheaters. Now the cheater detection is community-driven and only a few of cheaters are being detected.
Idea
I’m proposing Codeforces Anti‑Cheat (CFAC) – an automated flagging system that works after each contest and automatically detects cheaters using:
— NLP-model based submission (and maybe replacement) checking
— Timings-based detection: if gray solves div.2 e in 3 mins, its suspicious
all of these metrics are combined into suspicion score matrix where score[u][p] is value normalized [-1, 1] where
— -1 — if participant $$$u$$$ 100% not cheating at problem $$$p$$$;
— 1 — if participant $$$u$$$ 100% cheating at problem $$$p$$$;
Need help
I need help in
— collecting labelled data for cheater's code
— final testing of anti-cheat system
My review on my NLP-based model
It works pretty well, but it can detect only well-LLMed submissions like that:
Why it isnt working well?:
because my AI-generated samples were very-very simple to detect
because some LLMish things can be too difficult do detect using only CodeBERT-generated embeddings
As solution I will start everything from scratch to make my model detect more AI landmarks which are hard to see through embeddings
Updates
- Created cfac repo on github
- Updated post text without AI addressing hate comments about AI-slop and pilliamw blog post
- Major update: (finally) trained a model for classifying cheaters/not cheaters (not pushed changes to repo yet)







