Avatar Motion Segmentation for Bot Detection in Metaverses
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Solution Overview
Problem
The proliferation of bots in virtual digital environments, such as metaverses, poses challenges including misinformation, resource consumption, and security threats, necessitating effective detection and mitigation strategies.
Innovation Solution
A method involving tracking avatar motion within virtual environments, segmenting the motion into segments, comparing these segments to a database of movement patterns, and executing mitigation actions when a similarity metric exceeds a threshold to identify and counteract bot activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bot detection and mitigation systems are implemented in virtual environments, then security and reliability are improved, but device complexity and computational resource consumption increase
Solution Approach 1:
The bot detection system segments avatar motion into discrete movement segments that can be independently analyzed and compared against pattern databases. This segmentation allows the complex detection process to be broken down into manageable components, improving reliability while controlling system complexity.
Solution Approach 2:
The system pre-computes and stores movement patterns in databases before actual bot detection occurs. By preparing reference patterns in advance, the system reduces real-time computational complexity while maintaining high detection reliability through ready-to-use comparison data.
2Measurement precision
If comprehensive bot detection analysis is performed on all avatar motions, then detection precision is improved, but processing time and productivity are reduced
Solution Approach 1:
The system applies motion analysis selectively rather than uniformly to all avatar movements. By identifying and analyzing only suspicious or anomalous motion patterns, the system maintains high detection precision while reducing overall processing time and avoiding unnecessary analysis of normal movements.
Solution Approach 2:
Motion is divided into segments that can be independently evaluated. This allows the system to focus computational resources on specific segments that exhibit suspicious characteristics, improving detection precision for problematic areas while minimizing processing time for the entire motion sequence.
3Reliability
If motion tracking and pattern comparison are continuously performed, then bot detection reliability is improved, but energy consumption and computational load increase
Solution Approach 1:
The system performs motion tracking and pattern comparison at periodic intervals rather than continuously. This periodic analysis maintains detection reliability by regularly monitoring avatar behavior while significantly reducing computational energy consumption compared to continuous real-time analysis.
Solution Approach 2:
Full motion analysis is performed only when suspicious patterns are detected or at scheduled intervals. During normal operation, the system uses lighter monitoring, reducing energy consumption while maintaining reliability through periodic comprehensive checks and event-triggered analysis.
Data Source
AI summary
Disclosed embodiments provide bot detection in a virtual environment that is based on avatar motion. The motion of an avatar within a virtual environment such as a metaverse is tracked. The tracked motion is separated into multiple movement segments. The movement segments are compared to a collection of movement patterns in a movement pattern database that are indicative of bots. A similarity metric is determined between the tracked motion and the database of bot motions. In response to the similarity metric exceeding a predetermined threshold, a list of at least one mitigation action is obtained, and the at least one mitigation action in the list is executed, thereby mitigating the adverse effects of bots in a virtual digital environment.