Few-shot Learning for Trajectory-based Mobile Game Cheating Detection

Su, Yueyang; Yao, Di; Chu, Xiaokai; Li, Wenbin; Bi, Jun; Zhao, Shiwei; Wu, Runze; Zhang, Shize; Tao, Jianrong; Deng, Hao

doi:10.1145/3534678.3539157

Cited by 4 publications

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“…However, RNNs' sequential computation process leads to high computational complexity during training and inference. This limitation restricts the scalability and accuracy of RNNs when dealing with long sequences or large-scale tasks [22][23][24].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

VADNet: Visual-Based Anti-Cheating Detection Network in FPS Games

Nie,

2024

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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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Section: Deep Learning Methodsmentioning

confidence: 99%

VADNet: Visual-Based Anti-Cheating Detection Network in FPS Games

Nie,

2024

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“…There has been a lot of research [2], [5]- [7], [57], [58] on trajectory outlier detection and existing methods can be divided into two categories, i.e., metric-based methods and learning-based methods. The metric-based methods [8], [9], [13] identify anomalous trajectories based on their distance from other trajectories or reference trajectories.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Region-Wide Trajectory Outlier Detection

2023

IEEE Access

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Trajectory outlier detection is a crucial task in trajectory data mining and has received significant attention. However, the distribution of trajectories is tied to social activities, resulting in extreme unevenness among regions. While existing methods have demonstrated excellent performance in regions with sufficient historical trajectories, they frequently struggle to detect outliers in regions with limited trajectories. Unfortunately, this issue has not received much attention, leaving a gap in the current understanding of trajectory mining. To deal with this problem, we in this paper propose a model called TTOD that can effectively detect outliers in regions with sparse data by transferring knowledge among regions. The main idea is to learn a feature mapping function that maps the global feature space of auxiliary regions to the target region's specific feature space. To achieve this, we adopt a VAE-based model called the Global VAE to learn the global feature space in auxiliary regions by modeling the trajectory patterns with Gaussian distributions. Then, we propose a Specific-region VAE that serves as the mapping function to learn the target feature space. Additionally, considering the data drift of feature distributions among regions, we introduced an additional pattern synthesis layer, named the De-drift Layer, to diversify the target feature space, thus addressing the pattern missing issue caused by the gap of feature distributions between the auxiliary regions and the target regions. Then the target feature space can be well studied and applied to detect outliers. Finally, we conduct extensive experiments on two real taxi trajectory datasets and the results show that TTOD achieves state-ofthe-art performance compared with the baselines.INDEX TERMS trajectory outlier detection, transfer learning, VAE, spatial-temporal data, trajectory data mining.

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Deep learning applications in games: a survey from a data perspective

Hu,

Ding,

et al. 2023

Appl Intell

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Few-shot Learning for Trajectory-based Mobile Game Cheating Detection

Cited by 4 publications

References 0 publications

VADNet: Visual-Based Anti-Cheating Detection Network in FPS Games

VADNet: Visual-Based Anti-Cheating Detection Network in FPS Games

Transfer Learning for Region-Wide Trajectory Outlier Detection

Deep learning applications in games: a survey from a data perspective

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