Spatial domain-based nonlinear residual feature extraction for identification of image operations

In this paper, a novel approach that uses a deep learning technique is proposed to detect and identify a variety of image operations. First, we propose the spatial domain-based nonlinear residual (SDNR) feature extraction method by constructing residual values from locally supported filters in the spatial domain. By applying minimum and maximum operators, diversity and nonlinearity are introduced; moreover, this construction brings nonsymmetry to the distribution of SDNR samples. Then, we propose applying a deep learning technique to the extracted SDNR features to detect and classify a variety of image operations. Many experiments have been conducted to verify the performance of the proposed approach, and the results indicate that the proposed method performs well in detecting and identifying the various common image postprocessing operations. Furthermore, comparisons between the proposed approach and the existing methods show the superiority of the proposed approach.