基于快速神经网络架构搜索的鲁棒图像水印网络算法

为解决深度学习在图像水印算法中计算量大且模型冗余的问题，提高图像水印算法在抵抗噪声、旋转和剪裁等攻击时的鲁棒性，提出基于快速神经网络架构搜索（neural architecture search，NAS）的鲁棒图像水印网络算法。通过多项式分布学习快速神经网络架构搜索算法，在预设的搜索空间中搜索最优网络结构，进行图像水印的高效嵌入与鲁棒提取。首先，将子网络中线性连接的全卷积层设置为独立的神经单元结构，并参数化表示结构单元内节点的连接，预先设定结构单元内每个神经元操作的搜索空间；其次，在完成一个批次的数据集训练后，依据神经元操作中的被采样次数和平均损失函数值动态更新概率；最后，重新训练搜索完成的网络。水印网络模型的参数量较原始网络模型缩减了92%以上，大大缩短了模型训练时间。由于搜索得到的网络结构更为紧凑，本文算法具有较高的时间性能和较好的实验效果，在隐藏图像时，对空域信息的依赖比原始网络更少。对改进前后的2个网络进行了大量鲁棒性实验，对比发现，本文算法在CIFAR-10数据集上对抵抗椒盐噪声和旋转、移除像素行（列）等攻击优势显著；在ImageNet数据集上对抵抗椒盐高斯噪声、旋转、中值滤波、高斯滤波、JPEG压缩、裁剪等攻击优势显著，特别是对随机移除行（列）和椒盐噪声有较强的鲁棒性。

Robust image watermarking network algorithm based on effective neural architecture search

In order to solve the problem of large computation and model redundancy when applying deep learning to image watermarking,and to improve the robustness of image watermarking algorithm against noise,rotation,and cropping attacks,this paper adopts the robust image neural architecture search (NAS). Multinomial distribution learning for effective the neural architecture search watermarking network algorithm based on algorithm to find the optimal network structure in the preset search space,and performs efficient embed-ding and robust extraction of image watermarks. Firstly,the linearly connected fully convolutional layers in the sub-network are changed to independent neural unit structure,then we parameterize the connection of nodes in the structural unit,and set the search space for each neuron operation in the structural unit. Secondly,after completing a batch of data set training each time,the probabilities of being selected for the next operation are dynamically updated according to the number of samples and the average loss function value.Finally,the network structure after the search is retrained,which reduces the model parameters by more than 92% comparing with original model as well as the model training time. Since the obtained network structure is more compact,the proposed algorithm can achieve higher performance and better experimental results.Compared to the original network,the newly-developed network relies less on spatial information when hiding images.A larger number of experiments are performed to illustrate the advantages of the proposed algorithm including resisting salt and pepper noise,rotation and flipping,and removing pixel rows (columns) and other attacks on the CIFAR-10 data set; and has significant advantages against attacks of median filtering,Gaussian filtering,JPEG compression,and cropping on ImageNet,especially for the random removal of rows (columns) and salt and pepper noise.