Digital watermarking has shown its effectiveness in protecting multimedia content. However, existing watermarking is predominantly tailored for specific media types, rendering them less effective for the protection of content displayed on computer screens, which is often multi-modal and dynamic. Visual Screen Content (VSC), is particularly susceptible to theft and leakage through screenshots, a vulnerability that current watermarking methods fail to adequately address. To address these challenges, we propose ScreenMark, a robust and practical watermarking method designed specifically for arbitrary VSC protection. ScreenMark utilizes a three-stage progressive watermarking framework. Initially, inspired by diffusion principles, we initialize the mutual transformation between regular watermark information and irregular watermark patterns. Subsequently, these patterns are integrated with screen content using a pre-multiplication alpha blending technique, supported by a pre-trained screen decoder for accurate watermark retrieval. The progressively complex distorter enhances the robustness of the watermark in real-world screenshot scenarios. Finally, the model undergoes fine-tuning guided by a joint-level distorter to ensure optimal performance. To validate the effectiveness of ScreenMark, we compiled a dataset comprising 100,000 screenshots from various devices and resolutions. Extensive experiments on different datasets confirm the superior robustness, imperceptibility, and practical applicability of the method.

Bitwise Vision AutoRegressive (BVAR) Model, as a distinguished source of young blood, has been taking the lead in the track of text-to-image synthesis, which at the same time raises legal and ethnic concerns such as copyright and authenticity. However, existing methods mainly focus on watermarking within diffusion models, which rely on the distinctive attributes of diffusion steps and cannot be directly transferred to new circumstances. To this end, we propose Safe-BVAR, the first watermark framework to embed bit strings during image generation in BVAR. Our study discovers the local similarity of the inferenced latent feature and the element-wise robustness of image autoencoder. Therefore, combined with the residual-accumulative nature of BVAR, we propose a novel Late Stage Residual Implanter to embed watermark and extract the information based on Local Contextual Extractor. Furthermore, we propose a Distributed Rotational Arranger to enhance watermark against local distortions. Our method is training-free and plug-and-play. Meanwhile, it can be easily applied to flexible-sized images. We evaluate the robustness and invisibility of the watermark, showing that it can resist common image attacks and cast inappreciable influence on the image.

Screenshot, which is a common tool in office work, has become a significant threat to organizations like companies and research institutions. Malicious users can easily leak sensitive information like business secrets and research data by taking a screenshot and spreading onto the Internet. While existing watermarking schemes serve as useful tools for leakage tracing, they fall short in the scenario of arbitrary screenshot. Most current methods are file-targeted, focusing on embedding watermark for a single file of one type at a time, making it hard to handle arbitrary content on screen. To address the issues above and better satisfy the need of the scenario, we propose ScreenGuard, a novel watermarking scheme targeted for the screen itself to protect arbitrary screen content shown on it. Unlike previous watermarking schemes, ScreenGuard does not modify the content itself. Instead, we generate a transparent mask template based on the watermark, tile it to the size of the screen to form a complete transparent mask, and overlay this mask onto the screen. This ensures that any screenshots taken will contain our watermark. We then train a locator and a decoder to extract watermarks from suspected leaked screenshots to trace leaks to their source. We summarized five properties that needs to be satisfied in the scenario of arbitrary screenshot (Generalizable, Unseeable, Adaptable, Robust, Dynamic) and evaluate our method on these criteria. Extensive experiments demonstrate that ScreenGuard meets these five properties effectively, showcasing its superiority and broad practical applications.