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A master’s student at the Norwegian University of Science and Technology (NTNU), Chris Bjørke, reports that the mouse and keyboard behavior already recorded in Counter-Strike 2 match demos can be turned into player fingerprints that suggest when two accounts belong to the same person. The method is a proposal and a reported result from one thesis, not a system Valve runs. The thesis paper is not publicly available, and the reported accuracy has not been tested on a population the size of CS2’s player base.
What the method does
According to Bjørke’s public thesis-results post from 2026, every competitive match produces a demo, and that demo contains a tick-by-tick record of where each player looked and which buttons they pressed. The approach uses those existing demo files rather than new data from players’ computers. The method works in five stages:
- Read the demo. The system extracts per-tick mouse movement and keyboard input for each player in a match.
- Build two separate fingerprints. One fingerprint describes mouse habits and another describes keyboard habits. They are not merged into a single score.
- Compare across accounts. Fingerprints from one account are compared with fingerprints from other accounts.
- Require agreement. Both signals must independently point to the same person before two accounts are linked.
- Keep only recent fingerprints. The post says the system stores recent fingerprints rather than keeping the demos themselves.
The post says the approach needs no client changes, no new telemetry, and no kernel driver. It also says a new account needs several matches before the system can make a determination, so a single match is not enough to link anything.
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Every figure below comes from Bjørke’s 2026 post unless noted. None has been independently replicated, and the post does not provide the full evaluation details needed to check them.
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| Reported figure | What it describes | Source and date |
|---|---|---|
| More than 1,000 players | Size of the dataset used in the thesis work | Chris Bjørke, 2026 thesis-results post |
| 100% identification | Mouse signal picked the correct player every time in the reported dataset, often by a fine margin | Chris Bjørke, 2026 thesis-results post |
| 98% identification | Keyboard signal in the same reported dataset | Chris Bjørke, 2026 thesis-results post |
| 13 linked smurfs | Accounts identified in the dataset after the researcher investigated apparent false positives: eight submitted known linked pairs and five additional same-person pairs found through manual investigation | Chris Bjørke, 2026 thesis-results post |
| 0.11 correlation | Similarity between mouse habits and keyboard habits among pairs of unrelated players (strangers) | Chris Bjørke, 2026 thesis-results post |
A separate study is sometimes mentioned alongside this work. Tanglei Ni and colleagues published an IEICE technical report in 2026 on Counter-Strike behavioral authentication using replay-derived input patterns. It reports an AUC of 0.954 and an EER of 0.109, using match-level cross-validation. That study is independent of Bjørke’s thesis, and its metrics cannot be read as validation of his method. Neither study provides a head-to-head comparison with the other.
What the numbers do not show
The high identification percentages describe performance on the reported dataset. They do not establish how often the method would falsely link two unrelated players across CS2’s full player population. The researcher himself says that accuracy has not been tested on a real population of that size, and he writes: “Accuracy hasn’t been tested on a real population the size of CS2’s, and I would love the data to do it.”
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The evidence of same-person pairs is also limited. The researcher’s own assessment is: “The number of verified same-person pairs is small, clean but thin.” A set of 13 linked accounts is enough to show the idea can work on some cases, but not enough to estimate error rates with confidence.
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Two other limits are stated in the post:
- Shared accounts. The method assumes that each account reflects one person’s behavior. A shared account, where several people play on the same login, breaks that assumption.
- Short histories. A new account needs several matches before the method can make a determination.
The post reports robustness across some settings and months. It does not report results for players who deliberately change their behavior, or for players who swap mice or change sensitivity. Those questions remain open.
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Why a match is not proof that someone cheated
The method links accounts. It does not decide whether a linked account is a cheater, and it does not decide what happens next. The researcher frames a link as something that could inform a watchlist or be weighed alongside other evidence. The final action would be a platform policy decision.
The post raises several questions that such a policy would have to answer:
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- Should an account link lead to a watchlist, further review, or an extension of an existing ban?
- What additional evidence should be required before any action is taken?
- How long should any consequence last?
The researcher presents these as decisions for platform operators, not as results the identification model establishes. A link that points to a person is not the same as a finding that the person broke the rules.
Privacy and data handling
The researcher describes the input as existing game demos and says the system stores recent fingerprints rather than the demos. 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. It is not a legal ruling, and it does not settle how the data would be treated under any particular law or on a live platform. The post says the researcher considers consent and storage safeguards important.
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Common questions
Is this already part of VAC or Valve matchmaking?
The post describes a separate account-linking system proposed for matchmaking or anti-cheat platforms. Nothing in the cited material indicates that Valve has adopted it, and it should not be assumed to be part of VAC.
Can it prove a banned cheater returned on a new account?
Not on its own. A fingerprint match is evidence that two accounts may belong to the same person. It cannot establish intent, and it cannot establish that a person broke a rule, without other evidence and a policy decision.
How often could it falsely link strangers?
That is not established. The post reports a 0.11 correlation between mouse and keyboard similarity among stranger pairs, and it describes the false positives it investigated. It does not give a false-link rate for CS2’s full population.
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The method assumes one account reflects one person. The post names shared accounts as a weakness, and it does not describe a way to separate several users on one login.
Bottom line
Bjørke’s work shows that mouse and keyboard behavior captured in CS2 demos can be turned into fingerprints that link accounts in a dataset of more than 1,000 players. The accuracy figures are promising but come from one thesis, a small set of verified same-person pairs, and a dataset that has not been tested at the scale of CS2’s player base. Treat it as a proposed account-linking method that would still need broader validation, clear policy rules, and safeguards before it could responsibly inform bans.
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