Smooth filtering, a common blurring and denoising operator, has often been utilized postoperatively to diminish the traces left by malicious manipulations. Most of the existing forensic methods only focus on one specific filtering artifact such as median filtering, which is insufficient to reveal the manipulation history of digital images. Unlike traditional convolutional neural network (CNN)-based networks, which normally introduce handcrafted features, including frequency domain features and median filtering residuals, into the preprocessing layer, this paper proposes an end-to-end deep learning model for robust smooth filtering identification. First, a distinctive network structure named the Squeeze-and-Excitation (SE) block is introduced to select discriminative features adaptively and suppress the irrelevant features to the smooth filtering effect. Then, as the network depth increases, multiple inception-residual blocks are stacked to extract discriminative features and reduce the information loss. Finally, different smooth filtering operations can be classified through learning hierarchical features. The experimental results on a composite database show that the proposed model outperforms the state-of-the-art methods, especially in small size and JPEG compression scenarios.

平滑过滤是一种常见的模糊和去噪算子，经常在术后使用以减少恶意操作留下的痕迹。大多数现有的取证方法只关注一种特定的滤波伪像，例如中值滤波，不足以揭示数字图像的处理历史。与传统的基于卷积神经网络 (CNN) 的网络通常将手工特征（包括频域特征和中值滤波残差）引入预处理层不同，本文提出了一种端到端的深度学习模型，用于鲁棒的平滑滤波识别。第一的，引入了一种名为 Squeeze-and-Excitation (SE) 块的独特网络结构，以自适应地选择判别特征并抑制与平滑过滤效果无关的特征。然后，随着网络深度的增加，堆叠多个初始残差块以提取判别特征并减少信息丢失。最后，通过学习层次特征，可以对不同的平滑滤波操作进行分类。在复合数据库上的实验结果表明，所提出的模型优于最先进的方法，尤其是在小尺寸和 JPEG 压缩场景中。通过学习层次特征，可以对不同的平滑滤波操作进行分类。在复合数据库上的实验结果表明，所提出的模型优于最先进的方法，尤其是在小尺寸和 JPEG 压缩场景中。通过学习层次特征，可以对不同的平滑滤波操作进行分类。在复合数据库上的实验结果表明，所提出的模型优于最先进的方法，尤其是在小尺寸和 JPEG 压缩场景中。