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.

中文翻译：

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基于空间域的非线性残差特征提取用于图像识别

在本文中，提出了一种使用深度学习技术的新颖方法来检测和识别各种图像操作。首先，我们通过从空间域中的局部支持滤波器构造残差值，提出了基于空间域的非线性残差（SDNR）特征提取方法。通过应用最小和最大算子，引入了分集和非线性。而且，这种构造给SDNR样本的分布带来了不对称性。然后，我们建议对提取的SDNR特征应用深度学习技术，以检测和分类各种图像操作。已经进行了许多实验以验证所提出的方法的性能，并且结果表明所提出的方法在检测和识别各种常见图像后处理操作中表现良好。