Abstract: The multi-class classifier used in the existing source camera identification algorithms usually leads to numbers of problems, such as unavoidable false classification of the images out of the training models, decreasing accuracy as camera models increasing and the lack of expansibility. Focusing on these problems, a method for source camera identification based on the combination of one-class SVM and multi-class SVM is proposed in this paper. By solving covariance matrix equation, the authors reduce the perturbing term introduced by the pipeline of imaging, and improve the estimating precision of CFA interpolation coefficients. To obtain a more efficient feature space for classification, the sequential forward feature selection method is implemented to construct feature vector as the input of the classifier. The strategy using the combination of OC-SVM and MC-SVM as the classifier in the approach provide an effective approach for the classification of images out of training models and system’s expansibility. In the combination, the OC-SVM is used to expose the images that captured by an unknown camera model, and the MC-SVM trains a new multi-class model to classify the image source according to the positive results of the OC-SVM. The experiments indicate that average accuracies of 90.4% for camera model identification from 28 cameras, and 79.3% for three outlier camera model detection are obtained respectively in this method.