Blind Detection of Image Splicing Based on Fuzzy Run-Length

To improve the detection accuracy of spliced images, a new blind detection method based on fuzzy run-length is proposed in this study. Firstly, the edge gradient matrix of a digital image is calculated to obtain the gradient direction of each pixel and then quantified them to the vertical, horizontal, main diagonal, and minor diagonal directions. Then, according to the gradient direction of each pixel, histograms, such as the fuzzy run-length histogram, the fluctuation degree histogram and the fluctuation count histogram, are calculated. After that, the first three moments of the characteristic function of histograms are extracted as features for splicing detection. To further improve the detection accuracy, the fuzzy run-length is applied on original images, predict-error images and reconstructed images to extract a total of 45-D feature vector. Finally, the above features are trained and classified using SVM, by which the spliced images can be identified from the natural ones. The experimental results showed that, when testing on the Columbia image splicing detection dataset, the detection accuracy of the proposed method has improved substantially, which shows good growth prospects.