Image splicing detection has become one of the most important topics in the field of information security and much work has been done for that. We focus on its practical application, which considers not only detection rate but also the time consumption. This paper combines Run-length Histogram Features (RLHF) in spatial domain and Markov based features in frequency domain for capturing splicing artifact. Principal Component Analysis (PCA) is adopted to reduce the dimensions of the features in order to reduce the computational complexity in classification. Furthermore, this paper introduces Relevance Vector Machine (RVM) as a classifier and introduces its advantage over Support Vector Machine (SVM) in theory. Simulation shows that the performance of combined features is better than each feature alone. RVM consumes much less test time than SVM at the price of a negligible decline of detection rate. Therefore, the proposed method meets the requirements of a fast and efficient image splicing detection.
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