Deep learning-based watermarking methods have achieved a better performance in capacity and invisibility than some traditional methods. However, their robustness against JPEG lossy compression attack is still to be improved. To enhance the robustness and construct an end-to-end method, it is urgent to simulate the JPEG lossy compression by a neural network and then introduce it into the deep learning-based watermarking methods. In this paper, a JPEG simulation network JSNet is proposed to reappear the whole procedure of the JPEG lossy compression and restoration except entropy encoding as realistically as possible. The steps of sampling, DCT, and quantization are modeled by the max-pooling layer, convolution layer, and 3D noise-mask, respectively. The proposed JSNet can simulate JPEG lossy compression with any quality factors. To verify the proposed JSNet in improving the robustness against JPEG compression attack, a CNN-based robust watermarking network (CRWNet) is proposed as an application example. The end-to-end CRWNet contains three subnetworks, i.e., embedding subnetwork, JSNet, and extraction subnetwork. Here. the JSNet is regarded as an attack module in the pipeline. Experimental results on two publicly available datasets (ImageNet, BossBase) demonstrate that: (a) the proposed JSNet can well simulate JPEG lossy compression under any quality factors with small Root mean square error (RMSE) values; (b) the proposed CRWNet considering JSNet has achieved an average 30.6 percent advantage over the basic model without consideration of JSNet.

与某些传统方法相比，基于深度学习的水印方法在容量和不可见性方面取得了更好的性能。但是，它们针对JPEG有损压缩攻击的鲁棒性仍有待提高。为了增强鲁棒性并构建端到端方法，迫切需要通过神经网络模拟JPEG有损压缩，然后将其引入基于深度学习的水印方法中。本文提出了一种JPEG仿真网络JSNet，以尽可能真实地重现JPEG有损压缩和恢复的整个过程。采样，DCT和量化的步骤分别由最大池化层，卷积层和3D噪声蒙版建模。拟议的JSNet可以模拟具有任何质量因子的JPEG有损压缩。为了验证所提出的JSNet在提高针对JPEG压缩攻击的鲁棒性方面的作用，提出了一个基于CNN的鲁棒水印网络（CRWNet）作为应用示例。端到端的CRWNet包含三个子网，即嵌入子网，JSNet和提取子网。这里。JSNet被视为管道中的攻击模块。在两个公开可用的数据集（ImageNet，BossBase）上的实验结果表明：（a）所提出的JSNet可以在任何均方根值（RMSE）值较小的质量因子下很好地模拟JPEG有损压缩；（b）考虑到JSNet的拟议CRWNet较不考虑JSNet的基本模型获得了平均30.6％的优势。提出了基于CNN的鲁棒水印网络（CRWNet）作为应用示例。端到端的CRWNet包含三个子网，即嵌入子网，JSNet和提取子网。这里。JSNet被视为管道中的攻击模块。在两个公开可用的数据集（ImageNet，BossBase）上的实验结果表明：（a）所提出的JSNet可以在任何均方根值（RMSE）值较小的质量因子下很好地模拟JPEG有损压缩；（b）考虑到JSNet的拟议CRWNet较不考虑JSNet的基本模型获得了平均30.6％的优势。提出了基于CNN的鲁棒水印网络（CRWNet）作为应用示例。端到端的CRWNet包含三个子网，即嵌入子网，JSNet和提取子网。这里。JSNet被视为管道中的攻击模块。在两个公开可用的数据集（ImageNet，BossBase）上的实验结果表明：（a）所提出的JSNet可以在任何均方根误差（RMSE）值较小的质量因子下很好地模拟JPEG有损压缩；（b）考虑到JSNet的拟议CRWNet较不考虑JSNet的基本模型获得了平均30.6％的优势。在两个公开可用的数据集（ImageNet，BossBase）上的实验结果表明：（a）所提出的JSNet可以在任何均方根误差（RMSE）值较小的质量因子下很好地模拟JPEG有损压缩；（b）考虑到JSNet的拟议CRWNet较不考虑JSNet的基本模型获得了平均30.6％的优势。在两个公开可用的数据集（ImageNet，BossBase）上的实验结果表明：（a）所提出的JSNet可以在任何均方根误差（RMSE）值较小的质量因子下很好地模拟JPEG有损压缩；（b）考虑到JSNet的拟议CRWNet较不考虑JSNet的基本模型获得了平均30.6％的优势。