Aim: Aim of the research work is to detect image imitation using a Novel support vector machine using repositions data. Materials and Methods: The categorizing is performed by adopting a sample size of n = 10 in Novel Support Vector Machine and sample size n = 10 in Decision Tree algorithms with a sample size = 2, G power of 80%. Results: The analysis of the results shows that the Novel Support Vector Machine has a high accuracy of (95.563) in comparison with the Decision Tree algorithm (93.093). There is a statistically insignificant difference between the study groups with significance value p= 0.918 (p>0.05). Conclusion: Prediction in detection of Figure Imitation shows that the Novel Support Vector Machine appears to generate better accuracy than the Figure Imitation Decision Tree algorithm.