# Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations
## 1 Introduction
However, to choose the final classifier, most base classifiers are abandoned, and the chosen classifier is the classifier that has the minimum testing error; moreover, the classification ability of a single classifier is weak, and this could affect the detection ability of the final classifier. In our previous work [25], we employed the SRM and deep learning technology for image processing detection and identification, and the results indicated the great potential of applying deep learning technology. Currently, as a hot research field in machine learning, the deep learning technique has been widely used in multimedia security applications. In this paper, we propose applying the deep learning framework to the field of image forensics for the detection and classification of various operations. As part of the development of deep learning technology, the backpropagation neural network (BPNN) [26] classification method is a nonparametric classification method that is based on traditional statistical theory. It does not need to assume or estimate the probability distribution function of the target. Considering its good adaptability and complex mapping ability, this method can obtain good classification accuracy in pattern classification. However, considering the problem of the BPNN being fully connected in every layer, which leads to difficulty in training, the BPNN has been gradually replaced by the CNN [27]. The CNN proposes the concept of weight sharing, which increases the learning efficiency by greatly reducing the number of free parameters being learned. Thus far, a couple of neural network models have been proposed. LeNet-5 is a classic CNN that defines the classic neural network structure. AlexNet [28] extends the structure of LeNet-5, and it has made great progress in the field of image classification. However, AlexNet uses two layers of 4096 neurons to connect to each other, which makes the network easy to overfit and difficult to train. To avoid the abovementioned shortcomings and to effectively identify a variety of image operations, in this paper we propose a novel approach by extending our previous work [25]. In our previous work [25], we proposed this framework to identify the various image operations, and the experimental results showed a very good potential of the proposed framework in this application. In that work, the submodel of the spatial rich model (SRM) [23], sub_SRM, was applied for feature extraction. On that basis, in this work, 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, similarly, we apply a deep learning method of a five-layer CNN to the extracted SDNR features to produce the detection and identification results. Many experiments are conducted to evaluate the performance of the improved approach, and the results show that the improved work can identify more image postprocessing operations when compared with the previous work [25]; this previous work allowed seven operation types to be identified, while this improved method allowed more than twice this number to be identified. Furthermore, the results of using BPNN and AlexNet are tested to show the superiority of the proposed method. The remainder of this paper is arranged as follows: Section 2 presents the proposed approach for identification of image operations, Section 2.1 explains the principle of the proposed SDNR method, and Section 2.2 introduces the employed CNN classifier in detail. Then, Section 3 demonstrates the experiments and discussions. Finally, Section 4 concludes this paper.