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The SVM (support vector machine) and the SCM (skin color model) are used in detection of adult contents on images. The SVM consists of multi-class learning model and is very effective method for face detection, but complex. On the contrary, the SCM is very simple for detecting adult images using skin ratio derived from statistical characteristics of RGB color information, but less effective in close-up facial images. Hence, we propose a hybrid scheme that combines the SVM for the 1st filtering scheme using learning model (with classes of adult, benign and close-up facial images) with the SCM for the 2nd filtering scheme using skin ratio and adaptive MAP (maximum a posterior) hypothesis test based on Bayes' theorem that improves the probability of true positive detection rate of adult images.