Authorship verification using deep belief network systems
Bibliographic record
Abstract
Summary This paper explores the use of deep belief networks for authorship verification model applicable for continuous authentication (CA). The proposed approach uses Gaussian units in the visible layer to model real‐valued data on the basis of a Gaussian‐Bernoulli deep belief network. The lexical, syntactic, and application‐specific features are explored, leading to the proposal of a method to merge a pair of features into a single one. The CA is simulated by decomposing an online document into a sequence of short texts over which the CA decisions happen. The experimental evaluation of the proposed method uses block sizes of 140, 280, 500 characters, on the basis of the Twitter and Enron e‐mail corpuses. Promising results are obtained, which consist of an equal error rate varying from 8.21% to 16.73%. Using relatively smaller forgery samples, an equal error rate varying from 5.48% to 12.3% is also obtained for different block sizes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".