VADNet: Visual-Based Anti-Cheating Detection Network in FPS Games
Bibliographic record
Abstract
The prevalence of cheating in first-person shooter (FPS) games poses a formidable challenge, undermining user experience and the integrity of competitive play.In response to this issue, a visual-based anti-cheating detection network, termed VADNet, has been developed, harnessing the capabilities of deep learning and computer vision techniques.VADNet incorporates a focus module designed to segment high-resolution images, alongside a Feature Pyramid Network (FPN) for the fusion of multi-scale features, culminating in a classifier module tasked with the quantification of cheating behaviors.Rigorous experimentation on a dataset derived from a real online FPS game substantiates VADNet's efficacy in identifying players who resort to cheating, as evidenced by high precision, recall, and F1 scores.This investigation advances the field of anti-cheating mechanisms for FPS games, offering a robust and reliable system to preserve the fairness and integrity of online gaming environments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".