CshartMark: A Chart Watermarking Scheme With Consecutive-Encoding and Concurrent-Decoding

作     者：Ma, Linfeng Fang, Han Ma, Zehua Jia, Zhaoyang Zhang, Weiming Yu, Nenghai 

作者机构：Univ Sci & Technol China Anhui Prov Key Lab Digital Secur Hefei 230026 Peoples R China Univ Sci & Technol China CAS Key Lab Electromagnet Space Informat Hefei 230026 Peoples R China Natl Univ Singapore Sch Comp Singapore 117417 Singapore 

出 版 物：《IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY》 (IEEE Trans Circuits Syst Video Technol)

年 卷 期：2025年第35卷第1期

页      面：492-507页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：Natural Science Foundation of China [62121002, 62402469, 62472398, 62072421, U2336206] Fundamental Research Funds for the Central Universities [WK2100000041] 

主　　题：Watermarking Distortion Noise Robustness Training Decoding Feature extraction Digital watermark chart images neural networks consecutive-concurrent training 

摘      要：Chart images are widely employed as the intuitive form to express information, which renders them highly valuable. Consequently, there is an urgent demand to develop a watermarking algorithm for copyright protection and leakage prevention of chart images. Nevertheless, existing chart watermarking methods fail to thoroughly consider the chart image s special characteristics and simply rely on the previous natural image-based watermarking framework. Compared to natural images, the chart image generally exhibits relatively simple layouts and textures, containing fewer complex texture regions that watermarks are typically embedded in. Therefore, the embedding locations of watermarks for different distortions can be relatively dispersed in natural images, while for chart images, watermark embedding regions under various distortion conditions tend to be relatively concentrated and share more overlaps. Inspired by the above special characteristics of chart images, to sufficiently leverage them and design a better framework, this paper proposes C(3)hartMark, a chart watermarking scheme with consecutive-encoding and concurrent-decoding. Instead of using the combined noise layer as existing methods to ensure multiple robustness, a novel consecutive training framework is introduced in this paper, which efficiently utilizes the overlapping of embedded watermark features in chart images, and simultaneously, mitigates the poor convergence brought by the combined noise layer. During the extraction stage, multiple concurrent decoders are introduced to extract the potential embedded watermarks for different distortions independently. Moreover, we also incorporate two special noise layers, namely Captioning and Fusion, to address the corresponding realistic distortions in chart images, and an agnostic noise layer to accommodate potential channel transmission distortions unknown during training. Through extensive experiments, we demonstrate that with the better visual quality, C(3)har