# DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking
## 2 Related Work  
Due to the rapid development of machine learning and deep learning, more and more watermarking algorithms based on machine learning and deep learning are being proposed. Many machine learning methods have been introduced to improve imperceptibility and robustness. For example, to improve the imperceptibility of the algorithm, [PERSON] et al. [11] used an SVM to classify medical images into regions of interest and regions of noninterest. To improve robustness, [PERSON] et al. [13] trained an LSVR using low-frequency wavelet sub-band coefficients and used the trained LSVR to extract the watermark. [PERSON] et al. [14] used a K-NN regression algorithm to predict the optimal embedding strength of the watermark, balancing imperceptibility and robustness. Neural network frameworks for watermark embedding and extraction have also been widely proposed. For example, [PERSON] et al. [19] were the first to apply convolutional neural network technology to the field of watermarking. [PERSON] et al. [20] designed the first watermarking framework HiDDeN that could be trained end to end, which could be used for steganography and watermarking. [PERSON] et al. [21] used two watermark embedding methods, backpropagation and autoencoder, and the embedding strength was adjusted using a visual mask. [PERSON] et al. [22] divided the whole training process into two training stages: training the encoder without considering the noise and adjusting the decoder parameters according to the noise. [PERSON] et al. [23] introduced the channel coding module in the deep watermarking model to improve the robustness by increasing information redundancy. [PERSON] et al. [24] proposed a deep neural network suitable for multiresolution images and a variety of practical application scenarios. [PERSON] et al. [25] added an attack layer between the encoder and decoder to simulate multiple image distortion attacks for end-to-end training. [PERSON] et al. [26] randomly selected a real/simulated JPEG compression at the noise layer to achieve high robustness against JPEG compression attacks. [PERSON] et al. [27] trained the watermarking framework in an unsupervised way and achieved a better robustness without any prior knowledge. [PERSON] et al. [28] simulated image distortion operations on online social platforms through deep neural networks and learned lossy channel information to achieve a high robustness for the platforms. These methods improved robustness but did not strike a good balance between imperceptibility and robustness. To improve imperceptibility, the subjective feelings of people began to be considered instead of only using objective evaluation criteria, so the human visual system (HVS) and attention mechanisms began to be introduced into the image watermarking domain. For example, [PERSON] et al. [29] selected CIEDE2000, which is more in line with the HVS, as an image quality evaluation index and proposed the B-R-G embedding principle. [PERSON] [30] learned attention masks through convolutional neural networks to embed the watermark in locations that are insensitive to human vision. [PERSON] et al. [26] introduced the channel attention network in the watermark embedding and extraction process. [PERSON] et al. [31] combined wavelet decomposition and the HVS for significance modeling and adaptively embedding watermarks based on saliency. However, the attention mechanism in deep learning has not really been applied to watermarking algorithms. In this paper, we propose a deep learning and attention network for robust image watermarking: DARI-Mark. The channel and spatial attention network is introduced into the existing end-to-end watermarking framework, and through correct loss function settings and multiple training iterations, the network automatically finds human-insensitive texturally complex regions of the cover image to embed the watermark during watermark embedding, suppress noise signals and improve the extraction of watermark-related features during watermark extraction, improving imperceptibility and robustness.