Dynamic Bayesian Approach for Detecting Cheats in Multi-Player ...
Dynamic Bayesian Approach for Detecting Cheats in Multi-Player ...
Dynamic Bayesian Approach for Detecting Cheats in Multi-Player ...
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16 S.F. Yeung, John C.S. Lui<br />
probability (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
(a) cheater cb1<br />
probability (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
Inferred probability of cheat<strong>in</strong>g<br />
timeframe<br />
Inferred probability of cheat<strong>in</strong>g<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
timeframe<br />
(d) honest player hb1<br />
Accuracy (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
(g) cheater cb1<br />
Accuracy (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
Aim<strong>in</strong>g accuracy<br />
timeframe<br />
Aim<strong>in</strong>g accuracy<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
timeframe<br />
(j) honest player hb1<br />
probability (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
Inferred probability of cheat<strong>in</strong>g<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
(b) cheater cb2<br />
probability (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
timeframe<br />
Inferred probability of cheat<strong>in</strong>g<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
timeframe<br />
(e) honest player hb2<br />
Accuracy (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
(h) cheater cb2<br />
Accuracy (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
Aim<strong>in</strong>g accuracy<br />
timeframe<br />
Aim<strong>in</strong>g accuracy<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
timeframe<br />
(k) honest player hb2<br />
probability (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
Inferred probability of cheat<strong>in</strong>g<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
(c) cheater cb2<br />
probability (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
timeframe<br />
Inferred probability of cheat<strong>in</strong>g<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
timeframe<br />
(f) honest player hb3<br />
Accuracy (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
Aim<strong>in</strong>g accuracy<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
(i) cheater cb2<br />
Accuracy (%)<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
timeframe<br />
Aim<strong>in</strong>g accuracy<br />
0<br />
0 50 100 150 200 250 300 350 400 450 500<br />
timeframe<br />
(l) honest player hb3<br />
Fig. 6 Result of Experiment 1. Cheaters have probabilities fluctuat<strong>in</strong>g around the threshold, as illustrated <strong>in</strong> the sub-figures (a)-(c), while honest<br />
players have probabilities well below the threshold, as illustrated <strong>in</strong> sub-figures (d)-(f). For comparison, the aim<strong>in</strong>g accuracies of the players are<br />
shown <strong>in</strong> the sub-figures (g)-(l) respectively.
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