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A master’s student at the Norwegian University of Science and Technology (NTNU) reports that the way a player moves the mouse and presses keys, recorded in ordinary Counter-Strike 2 match demos, can indicate that two accounts are probably operated by the same person. The method, described by Chris Bjørke in a public post about his thesis results (2026), builds a separate fingerprint from each input type and links accounts only when both fingerprints agree. It is a proposed account-linking tool for matchmaking or anti-cheat teams. It is not a shown ability to prove that a banned player returned, and the full thesis is not publicly available.
How the method works
Every competitive match produces a demo, and according to Bjørke that demo holds a tick-by-tick record of where each player looked and which buttons they pressed. The proposed pipeline runs in four stages:
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- Extract the inputs. Mouse movement and keyboard presses are pulled from each player’s stream in the demo.
- Build two fingerprints. The mouse signal and the keyboard signal are modelled separately, so each produces its own player profile.
- Compare across accounts. Profiles from one account are checked against profiles from other accounts.
- Require agreement. An account link is proposed only when the mouse and keyboard comparisons independently point to the same person.
Bjørke says the approach uses existing demos and needs no client changes, no new telemetry, and no kernel driver. Because the data is produced by the game and stored on the server side, the method does not depend on anything running on a player’s computer. Demos are only available after a match, which is why the system needs several matches per account before it can make a determination (the number of matches is not stated in the post).
The reported results
All figures below come from Bjørke’s 2026 post on his thesis results unless marked otherwise. They describe a single dataset, and none has been independently reproduced.
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| Figure | What it measures | Qualification |
|---|---|---|
| More than 1,000 players | Size of the dataset used in the study | A study sample, not the full CS2 player population |
| 100% mouse identification | The mouse signal picked the correct player every time | Within the reported dataset only; the post notes that several matches were won by a fine margin |
| 98% keyboard identification | Correct player identified from keyboard behavior | Within the reported dataset only |
| 13 linked smurfs | Same-person account pairs found in the dataset after apparent false positives were investigated | Eight were known linked pairs submitted by the researcher; five more were found through manual investigation. This is a small set of verified pairs. |
| 0.11 correlation | Similarity in mouse habits against similarity in keyboard habits, among stranger pairs | Low correlation means the two signals mostly do not move together between different people. That is the property that makes requiring both to agree a meaningful check; it is an interpretation, not a separate claim from the post. |
| AUC 0.954 and EER 0.109 | Performance of a different system: a behavioral-authentication model for professional Counter-Strike players, reported by Tanglei Ni and colleagues in an IEICE technical report (2026) | Uses match-level cross-validation. It is a separate study, not a replication of Bjørke’s thesis, and its figures cannot stand in for his results. |
What the results do not establish
Bjørke is direct about the gaps, and they matter more than any single percentage:
- Scale is untested. The method has not been run on a population the size of CS2’s. In his words: “Accuracy hasn’t been tested on a real population the size of CS2’s, and I would love the data to do it.”
- The verified pair set is small. “The number of verified same-person pairs is small, clean but thin.” Accuracy figures built on a few dozen confirmed pairs cannot tell you how often strangers would be wrongly linked across millions of accounts.
- False-link rates at population scale are unknown. The reported 0.11 correlation is evidence about stranger pairs in one sample, not a rate that applies to all of CS2.
- Nothing has been replicated. The thesis is not publicly available in the post, so outside researchers cannot check the model, the data split, or the thresholds.
Where the method is weakest
Shared accounts
The method assumes that each account reflects the input habits of one person. Shared accounts break that assumption, because the fingerprint on the account then blends several people. Bjørke names this as a stated weakness, and the reported work does not describe how a shared account would be detected or separated.
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New accounts
An account with few matches gives the system little to work with. The researcher says a new account needs several matches before the method can make a determination, so a freshly created account cannot be linked on day one.
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Changes in settings or hardware
Bjørke reports robustness across some settings and across months of play. The post does not establish whether a different mouse, a new sensitivity setting, or a changed keyboard layout would stop a link from being made, and buying or changing peripherals is not shown to affect the outcome either way.
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Privacy and data handling
According to Bjørke, the system works from existing game demos but stores recent fingerprints rather than keeping the demos themselves. He says he consulted NTNU’s ethics and data-protection service, which judged the study’s data not to be biometric data because the game engine had transformed the inputs. That is the researcher’s account of the service’s view, not a legal ruling, and it does not settle how the same approach would be classified under other laws or on a platform with a different data model. Bjørke also says consent and storage safeguards are important to him, but the post does not describe what consent would look like for players in a live system.
From a link to a decision
Bjørke frames an account link as one input to a platform’s process, not as a verdict. The points he and the reported work separate are:
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- A link is evidence, not proof of cheating. It says two accounts may belong to one person. It does not show what that person did.
- The consequence is a policy choice. A link could feed a watchlist, prompt further review, or be weighed alongside other evidence in a ban extension. Each option is a decision for the platform operator.
- Additional evidence and duration are open questions. The post raises, without answering, what other evidence should be required before action and how long any consequence should last.
Is this already part of VAC or matchmaking?
Nothing in the reported work says Valve uses this method, and the post presents it as a proposal for matchmaking or anti-cheat platforms. Readers should treat it as a research result for a possible system, not as a feature of Valve Anti-Cheat or Counter-Strike 2 matchmaking today.
What would settle the question
Three kinds of evidence would move this from a promising result to something a platform could rely on: a validation run on a population close to CS2’s size, a published account of false-link rates for stranger pairs, and testing of how shared accounts and account changes affect results. The separate 2026 IEICE work shows that replay-derived input patterns can support player authentication in professional play, which makes the general direction plausible. It does not validate Bjørke’s model, and the two studies have not been compared head to head.
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