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.

The proliferation of digital images on the Internet has provided unprecedented convenience, but also poses significant risks of malicious theft and misuse. Digital watermarking has long been researched as an effective tool for copyright protection. However, it often falls short when addressing partial image theft, a common yet little-researched issue in practical applications. Most existing schemes typically require the entire image as input to extract watermarks. However, in practice, malicious users often steal only a portion of the image to create new content. The stolen portion can have arbitrary shape or content, being fused with a new background and may have undergone geometric transformations, making it challenging for current methods to extract correctly. To address the issues above, we propose WOFA (Watermarking One for All), a robust watermarking scheme against partial image theft. First of all, we define the entire process of partial image theft and construct a dataset accordingly. To gain robustness against partial image theft, we then design a comprehensive distortion layer that incorporates the process of partial image theft and several common distortions in channel. For easier network convergence, we employ a multi-level network structure on the basis of the commonly used embedder-distortion layer-extractor architecture and adopt a progressive training strategy. Abundant experiments demonstrate that our superior performance in the scenario of partial image theft, offering a more reliable solution for protecting digital images against unauthorized use in practical use.

Graphics Interchange Format (GIF) encoding is the art of reproducing an image with limited colors. Existing GIF encoding schemes often introduce unpleasant visual artifacts such as banding artifact, dotted-pattern noise and color shift, especially when the palette size is small. To address the issues above, we propose VivID, a Visually Improved GIF Encoding Network Design, which is compatible with exiting GIF decoders. VivID consists of three modules and two of them provide the functionality within the GIF encoding pipeline. Firstly, in order to reduce the color shift introduced by color quantization, we design the multi-palette extractor to create a GIF image with minimal distortion by extracting a near-optimal palette. This module can significantly improve the image fidelity and gains adaptability to multiple palette sizes after only one-time training. Furthermore, to reduce banding artifact and the dotted-pattern noise caused by dithering process, we propose banding remover which can randomize quantization error to neighbourhood by utilizing a learnable dithering pattern. Moreover, to further eliminate the banding artifacts, we design the banding scorer module, which is a novel metric for evaluating banding artifact and it correlates well with subjective perception. We adopt it as a customized loss for training dithering module. Extensive experiments across various aspects demonstrate that VivID produces visually pleasing results even when the palette size is extremely small, outperforming both traditional and existing learning based GIF encoding methods.

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.

Recently, print-resist watermarking has attracted much interest. Many watermarking schemes have been proposed to achieve robustness against printing and camera-capturing. Though these studies have shown promising results overall, they overlook the scenario of film-coating photographs, which is a significant and common scenario in real-world. The film-coating process can introduce severe distortions to the original image and easily incapacitate the watermark. To address this issue, we propose WRAP, a novel Watermarking scheme Robust Against film-coating upon Printed photographs. We first construct a large dataset with 120,000 film-coating images to train a style-transfer-based film-coating simulation network. Based on the network, we propose a comprehensive distortion layer which includes film-coating simulation and common disturbances in the printing and camera-capturing process. With the distortion layer, the entire embedding and extraction network can be trained end-to-end to gain robustness against film-coating upon printed photographs. Extensive experiments demonstrate the superior performances of our model in terms of robustness and generalization capability. Our model outperforms state-of-the-art print-resist watermarking schemes when testing in film-coating scenario and achieves outstanding performance across various datasets, types of films, and cameras. To the best of our knowledge, we are the first to conduct research on digital watermarking in film-coating scenario.

How to deal with the adversarial examples attracts a lot of interest recently. In this paper, we propose HF-Defend: a novel method to defend against the adversarial examples based on halftoning. Unlike the existing schemes, HF-Defend thoroughly removes the adversarial perturbations by transforming a 8-bit grayscale image (or one of the RGB channels in a color image) into a 1-bit halftoned image. To maintain the image quality and content, we propose a reconstruction module for the recovery of both the main contents and fine details of the image. In particular, we newly design a nonlinear low-pass filter to extract the main contents, and a FilterNet to establish a high-pass filter for the reconstruction of fine details. Experimental results demonstrate the advantage of our HF-Defend over the existing schemes.