# Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model
###### Abstract  
Over recent years, there are an increasing number of incidents in which archival images have been ripped. Leak tracking is one of the key problems for anti-screenshot digital watermarking of archival images. Most of the existing algorithms suffer from low detection rate of watermark, because the archival images have a single texture. In this paper, we propose an anti-screenshot watermarking algorithm for archival images based on Deep Learning Model (DLM). At present, screenshot image watermarking algorithms based on DLM can resist screenshot attacks. However, if these algorithms are applied on archival images, the bit error rate (BER) of the image watermark will increase dramatically. Archival images are ubiquitous, so in order to improve the robustness of archival image anti-screenshot, we propose a screenshot DLM \"ScreenNet\". It aims to enhance the background and enrich the texture with style transfer. Firstly, a preprocessing process based on style transfer is added before the insertion of an archival image into the encoder to reduce the influence of the screenshot process of the cover image. Secondly, the ripped images are usually moired, so we generate a database of ripped archival images with moired by means of moired networks. Finally, the watermark information is encoded/decoded through the improved ScreenNet model using the ripped archive database as the noise layer. The experiments prove that the proposed algorithm is able to resist anti-screenshot attacks and achieves the ability to detect watermark information to leak the trace of ripped images. image watermarking; anti-screenshot; Stegastamp; DLM; archival image 202325288. [[https://doi.org/10.3390/e2502028810.3390/e2502028810.3390/e2502028810.3390/e250202810](https://doi.org/10.3390/e2502028810.3390/e2502028810.3390/e2502028810.3390/e250202810)]([https://doi.org/10.3390/e2502028810.3390/e2502028810.3390/e2502028810.3390/e250202810](https://doi.org/10.3390/e2502028810.3390/e2502028810.3390/e2502028810.3390/e250202810)). definitely put into the archival images. Therefore, personnel management can work better in a uniform way. Some workplaces are required to provide archival images for recruitment. This can be a serious problem if the copyright of archival images is not well-protected. Image watermarking algorithms should have strong robustness because the watermarked image may be attacked in the transmission process. Common attacks include cropping, compression, Gaussian noise, etc., all of which may change the pixel value of the watermark image, and then the watermark information cannot be extracted smoothly. Screenshot attack is a very complex image attack that includes perspective deformation and a moire pattern. In this paper, the noise between the screen and the camera exists commonly. However, the traditional robust watermarking algorithms show low robustness, so it is necessary to introduce a DLM to improve the performance of the algorithm. Recently, DLMs such as Stegasatamp [1] have been widely applied in computer vision, natural language processing and other fields. DLMs have achieved excellent results in experiments. Therefore, researchers also explore image watermarking algorithm based on DLM. [PERSON] et al. [2] initiated the HiDDeN model, which introduces a variety of noises between the encoder and decoder to improve the robustness of the auto-encoder. However, since the noise layer only considers Gaussian blur, cropping, JPEG compression and other attacks, it cannot effectively resist the problems of a moire attack, perspective deformation and color transformation. In order to simulate screenshot attacks in real scenes, [PERSON] et al. [3] proposed the light field messaging (LFM) system. LFM specially trains a network to simulate the effect of cover image to screenshot process, so a dataset of 1,000,000 images is established. However, the dataset takes up a lot of memory and it is too difficult to obtain. Moreover, LFM has a good effect on screenshot attacks, and it has limitations for print attacks.