Submovement Segmentation for Mouse Cheat Detection
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Solution Overview
Problem
Existing methods fail to effectively detect cheating in video games, particularly when cheating tools inject synthetic mouse motion that mimics human input, evading detection by traditional trusted execution environments.
Innovation Solution
A cheating detection strategy that segments motion data into submovements and analyzes attributes such as submovement count, duration, velocity, and error to differentiate between human and synthetic motion, using statistical models to flag deviations from normal human performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trusted execution environment methods are used to detect cheating, then system security is maintained, but cheating detection accuracy deteriorates because synthetic mouse motion cannot be distinguished from human input
Solution Approach 1:
The patent segments continuous mouse motion data into discrete submovements based on velocity thresholds and temporal patterns. By dividing the motion stream into atomic units with specific characteristics (duration, amplitude, acceleration), the system can analyze individual submovement properties to detect synthetic patterns that differ from human physiology, thereby improving cheating detection accuracy without requiring overly complex overall system architecture
Solution Approach 2:
The patent transforms mouse motion detection from position-based analysis to parameter-based analysis by extracting multiple attributes including submovement duration, velocity, acceleration, jerk, and inter-submovement intervals. By monitoring changes in these parameters and comparing them against established human physiological ranges, the system can identify synthetic motion patterns while maintaining manageable system complexity through standardized parameter measurement
2Measurement precision
If motion data is segmented into multiple attributes for analysis, then cheating detection precision improves, but processing time increases
Solution Approach 1:
The patent performs preliminary segmentation of mouse motion data into submovements as data arrives, organizing it into structured units with pre-calculated basic attributes (position, velocity, timestamp). This preliminary organization allows subsequent analysis of higher-order attributes like acceleration and jerk to be performed efficiently on already-processed segments, reducing redundant calculations and minimizing processing time while maintaining high measurement precision
Solution Approach 2:
The patent implements a tiered analysis approach where only essential submovement attributes are calculated for all inputs (position, velocity, duration), while more computationally intensive attributes (acceleration, jerk, curvature) are calculated selectively based on anomaly detection needs or specific detection scenarios. This partial action approach maintains high measurement precision where needed while reducing overall processing time by avoiding unnecessary calculations for normal inputs
Data Source
AI summary
A cheat detection methodology is disclosed that relates to identifying cheaters making super-human movements in interactive programs. For example, users trying to outcompete their opponents in video games using large aim assists and aim bots that perform actions that are not feasibly human. The disclosed methodology substantially reduces or even eliminates the benefit that various cheating solutions offer. In one aspect, the disclosure provides a method of monitoring cheating in interactive programs. In one example, the method includes: (1) obtaining motion data corresponding to a user input device interacting with an interactive program, (2) segmenting data submovements from the motion data, (3) determining one or more attributes of the data submovements, and (4) detecting, based on the one or more attributes, the data submovements that are a deviation of human submovements.