Fingerprint identification systems have been widely applied in both civilian and governmental applications due to its satisfying performance. However, the fingerprint identification systems can be easily cheated by the presentation of artificial fingerprints made from common materials. Therefore, it reduces the reliability and misleads the decision of the fingerprint identification systems. In this work, we propose a software-based fingerprint liveness detection method based on multiscale difference co-occurrence matrix (DCM). In doing so, multiscale wavelet transform operation is first conducted on the original image. After the preprocessing of the decomposition of the original image, DCMs are computed by using the Laplacian operator. Horizontal and vertical difference co-occurrence matrices are constructed in our method. In order to reduce the dimensionality of the feature vectors, truncation operation is introduced for DCMs. Then, the elements of processing DCMs are regarded as the texture features of original fingerprint images. Finally, classification accuracy of feature vectors is predicted based on a support vector machine classifier. The experimental results have shown that the performance of our method is very promising and meanwhile achieve better accurate classification compared with the best algorithms of LivDet2013 and LivDet2011, while being able to recognize spoofed fingerprints with better recognition accuracy.