Aim: The 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 CNN 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 (92.80) in comparison with the CNN algorithm (88.14). There is a statistically insignificant difference between the study groups with significance value p= 0.701 (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 CNN algorithm.