The wide use of high-performance image acquisition devices and powerful image-processing software has made it easy to tamper images for malicious purposes. Image splicing, which has constituted a menace to integrity and authenticity of images, is a very common and simple trick in image tampering. Therefore, image-splicing detection is of great importance in digital forensics. In this paper, an effective framework for revealing image-splicing forgery is proposed. First, the local binary pattern operator is used to model magnitude components of two-dimensional arrays obtained by applying multisize block discrete cosine transform to test images. Then, all of bins of histograms computed from local binary pattern codes are served as discriminative features for image-splicing detection. After that, kernel principal component analysis is utilized to reduce the dimensionality of the proposed features to avoid the high computational complexity, high mutual correlation among the constructed features and possible overfitting for support vector machine classifier. Finally, support vector machine classifier is employed to distinguish spliced images from authentic images by using the final dimensionality-reduced feature set. The experiment results show that the proposed method can perform better than some state-of-the-art methods in terms of the detection performance over the Columbia image-splicing detection evaluation dataset. Copyright © 2013 John Wiley & Sons, Ltd.