# Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model
###### Abstract
Many researchers apply the DLM directly as the encoder or decoder. [PERSON] et al. [4] generated several centrosymmetric Gaussian noises on the red channel of the color image to determine the embedding position of the watermark information. They trained a decoder with DLM to extract the watermark on the blue channel of the color image. To resist the screenshot attack, [PERSON] et al. [4] designed an enhancing subnet to improve the accuracy of watermark extraction. In a subsequent work, they applied the anti-screenshot DLM to dynamic images [5]. This algorithm embeds watermark information in the red or blue channel of the image in turn. Due to the frequent switching of the image in a short time, the color interchanges make the watermark information undetectable to the human eye. [PERSON] et al. [5] proposed DLM-based decoder with attention mechanism to improve the accuracy of the model. Consequently, their algorithm has significantly improved in imperceptibility and robustness compared with [4]. DLM can be used not only to embed or extract watermark information directly, it can also be used to assist in image watermark extraction. [PERSON] et al. [6] used DLM for finding feature points that can resist screenshot attacks. The watermark information is embedded in the region around the feature points, so feature point localization has a very important position in watermark extraction. DLM can find the feature points that contain the image watermark in the case of screenshot attack on the image. Additionally, [PERSON] et al. [7] added an anti-screenshot noise layer to the end-to-end DLM. The noise layer is presented between the encoder and decoder and used to simulate possible attacks on watermarked images. This increases the training time of the DLM to some extent, but the robustness of the anti-screenshot watermarking algorithm is significantly improved. This ensures that when a screenshot is taken of a watermarked image, the decoder can still extract the watermark information accurately. [PERSON] et al. [1] proposed the Stegasatamp model, in which the differentiable image perturbations were applied between the encoder and decoder to simulate the distortion caused by print attacks and screenshot attacks. The excellent watermarking algorithm is robust to both screen attack and print attack, but the Stegasatamp model is low robustness against the cropping attack. Therefore, the image cropping attack has limitations, which leads to the poor quality of a dense image. In this paper, we propose a robust archival image watermarking algorithm based on the Stegastamp model. The proposed algorithm structure includes five parts: the preprocessing network adds color and texture into the archival image. The encoder embeds the watermark information into the cover image successfully. It also minimizes the difference between the watermarked image and the cover image, and finally generates the watermarked image with good visual quality. The noise layer of Stegastamp is designed to distribute the watermarked image generated by the encoder to simulate the effect of screenshot attacks. The purpose of the moire network is to further add real moire to the distributed watermarked image. The decoder is to extract watermark information from the moire-watermarked image. The main contributions of this paper are as follows:  
1. A style transfer network is applied to improve the richness of background texture for archival images. So, the robustness of the image watermarking algorithm can be maintained. 2. A moire network is constructed to produce the moire archival image dataset. In addition, moire images taken in realistic environments are added to build a rich database of screenshot images. 3. The anti-screenshot capability of the Stegastamp model is improved by placing these datasets in the noise layer. It solves the problem of a lack of standard moire dataset in digital watermarking field.