System and method for cheat detection
Disclosed herein are system, method, and computer program product embodiments for assessing performance of a player of a game. An embodiment operates by monitoring for an input from the player of the game, receiving the input from the player of the game, and determining a characteristic of the game resulting from the input from the player. Based on the input from the player, a performance of the player is assessed. The performance of the player relating to one or more metrics of the game is monitored, and is assessed by comparing the input from the player during the period of time to an optimal input during the period of time in the game.
1. A computer-implemented method for cheat detection of a player of a game, the method comprising:
monitoring, by at least one computer processor, a plurality of inputs in a game by a one or more human players using at least one computer input device;
performing an analysis on the plurality of inputs, the analysis based on at least one of a distance between a projectile and a center of a target; and a distance from a movement trajectory and the center of a target; and
comparing an analysis on a second one or more inputs to the game with the analysis on the plurality of inputs to classify the second one or more inputs to the game as that of a human or a cheater.
2. The method of claim 1 , wherein the analysis on the plurality of inputs is a statistical analysis and the analysis on the second one or more inputs to the game is a statistical analysis.
3. The method of claim 1 , wherein the analysis on the plurality of inputs comprises a principal component analysis (PCA) and the analysis on the second one or more inputs to the game comprises a PCA.
4. The method of claim 1 , wherein each of the plurality of inputs is represented as a vector of numbers.
5. The method of claim 1 , wherein each of the analysis on the plurality of inputs and the analysis on the second one or more inputs to the game is selected from the group consisting of: pattern recognition, pattern classification, and machine learning.
6. The method of claim 1 , wherein the analysis on the second one or more inputs to the game comprises comparing the second one or more inputs with a database of natural human inputs.
7. The method of claim 1 , wherein the at least one computer input device comprises a device selected from the group consisting of: a mouse; a joystick; an accelerometer; a pointing device; a motion capture device; a Wii remote controller; an eye tracker; and a computer vision system.
8. The method of claim 1 , wherein the analysis of the distance between a projectile and a center of a target comprises the precision or variability of the distance between a projectile and a center of a target; and the analysis of the distance from a movement trajectory and the center of a target comprises the precision or variability of the distance from a movement trajectory and the center of a target.
9. The method of claim 1 , wherein an output of the analysis of each input is represented as a vector of numbers.
10. A device for cheat detection of a player of a game, the device comprising:
a memory; and
a processor to:
monitor a plurality of inputs in a game by a one or more human players using at least one computer input device;
perform an analysis on the plurality of inputs, the analysis based on at least one of a distance between a projectile and a center of a target; and a distance from a movement trajectory and the center of a target; and
compare an analysis on a second one or more inputs to the game with the analysis on the plurality of inputs to classify the second one or more inputs to the game as that of a human or a cheater.
11. The device of claim 10 , wherein the analysis on the plurality of inputs is a statistical analysis and the analysis on the second one or more inputs to the game is a statistical analysis.
12. The device of claim 10 , wherein the analysis on the plurality of inputs comprises a principal component analysis (PCA) and the analysis on the second one or more inputs to the game is comprises a PCA.
13. The device of claim 10 , wherein each of the plurality of inputs is represented as a vector of numbers.
14. The device of claim 10 , wherein each of the analysis on the plurality of inputs and the analysis on the second one or more inputs to the game is selected from the group consisting of: pattern recognition, pattern classification, and machine learning.
15. The device of claim 10 , wherein the analysis on the second one or more inputs to the game comprises comparing the second one or more inputs with a database of natural human inputs.
16. The device of claim 10 , wherein the at least one computer input device comprises a device selected from the group consisting of: a mouse; a joystick; an accelerometer; a pointing device; a motion capture device; a Wii remote controller; an eye tracker; and a computer vision system.
17. The device of claim 10 , wherein the analysis of the distance between a projectile and a center of a target comprises the precision or variability of the distance between a projectile and a center of a target; and the analysis of the distance from a movement trajectory and the center of a target comprises the precision or variability of the distance from a movement trajectory and the center of a target.
18. The device of claim 10 , wherein an output of the analysis of each input is represented as a vector of numbers.
19. A computer-implemented method for cheat detection of a player of a game, the method comprising:
monitoring, by at least one computer processor, a plurality of inputs into a game by one or more human players; and
comparing an analysis on one or more new inputs to the game with an analysis on the plurality of inputs to determine if the one or more new inputs are that of a human or that of a cheater, each analysis based on at least one of a distance between a projectile and a center of a target; and a distance from a movement trajectory and the center of a target.
20. The method of claim 19 , wherein the inputs are input via a device selected from the group consisting of: a mouse; a joystick; an accelerometer; a pointing device; a motion capture device; a Wii remote controller; an eye tracker; and a computer vision system.
21. The method of claim 19 , wherein an output of the analysis of each input is represented as a vector of numbers.
22. The method of claim 19 , wherein the analysis on the plurality of inputs is a statistical analysis and the analysis on the one or more new inputs input to the game is a statistical analysis.
23. The method of claim 19 , wherein the analysis on the plurality of inputs comprises a principal component analysis (PCA) and the analysis on the one or more new inputs input to the game is comprises a PCA.
24. The method of claim 1 , wherein each analysis comprises determining a performance metric.