基于修改汉字笔画分量的鲁棒文本水印

IF 2 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hai Chen, Yanli Chen, Zhicheng Dong, Yongrong Wang, Asad Malik, Hanzhou Wu
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引用次数: 0

摘要

传统的用于跟踪文本文档中信息泄漏的码本由于其手工生成过程,在嵌入容量、鲁棒性和效率方面存在一定的限制。提出了一种基于汉字笔画分量的鲁棒文本水印方法。通过设计一种创新的方法,汉字笔画被分成几个不同的组成部分,只有特定的部分被选择性地修改以产生新的字形,从而形成一个独特的密码本。用新生成的载体符号替换载体符号嵌入水印信号,并采用模板匹配方法提取水印信号。实验结果表明,与传统手工设计的码本相比,该方法在保持高视觉质量的同时,显著减少了人工劳动和计算开销。此外,它在各种具有挑战性的场景中表现出卓越的鲁棒性和适应性,包括数字噪声攻击、打印扫描攻击和打印相机捕获,使其成为保护文本信息的高效解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Robust Text Watermarking Based on Modifying the Stroke Components of Chinese Characters

Robust Text Watermarking Based on Modifying the Stroke Components of Chinese Characters

Traditional codebooks used for tracing information leakage in text documents often suffer from limitations in embedding capacity, robustness, and efficiency due to their manual generation process. This paper proposes a robust text watermarking method based on the stroke components of Chinese characters. By designing an innovative approach, Chinese character strokes are divided into several distinct components, with only specific ones being selectively modified to generate new glyphs, thus forming a unique codebook. The watermark signals are embedded by substituting the carrier glyph with the newly generated one, and the signals are extracted using a template matching method. Experimental results demonstrate that, compared to traditional manually designed codebooks, the proposed method significantly reduces human labor and computational overhead while maintaining high visual quality. Moreover, it exhibits superior robustness and adaptability across various challenging scenarios, including digital noise attacks, print-scanning attacks, and print-camera capture, making it a highly effective solution for protecting textual information.

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来源期刊
IET Image Processing
IET Image Processing 工程技术-工程:电子与电气
CiteScore
5.40
自引率
8.70%
发文量
282
审稿时长
6 months
期刊介绍: The IET Image Processing journal encompasses research areas related to the generation, processing and communication of visual information. The focus of the journal is the coverage of the latest research results in image and video processing, including image generation and display, enhancement and restoration, segmentation, colour and texture analysis, coding and communication, implementations and architectures as well as innovative applications. Principal topics include: Generation and Display - Imaging sensors and acquisition systems, illumination, sampling and scanning, quantization, colour reproduction, image rendering, display and printing systems, evaluation of image quality. Processing and Analysis - Image enhancement, restoration, segmentation, registration, multispectral, colour and texture processing, multiresolution processing and wavelets, morphological operations, stereoscopic and 3-D processing, motion detection and estimation, video and image sequence processing. Implementations and Architectures - Image and video processing hardware and software, design and construction, architectures and software, neural, adaptive, and fuzzy processing. Coding and Transmission - Image and video compression and coding, compression standards, noise modelling, visual information networks, streamed video. Retrieval and Multimedia - Storage of images and video, database design, image retrieval, video annotation and editing, mixed media incorporating visual information, multimedia systems and applications, image and video watermarking, steganography. Applications - Innovative application of image and video processing technologies to any field, including life sciences, earth sciences, astronomy, document processing and security. Current Special Issue Call for Papers: Evolutionary Computation for Image Processing - https://digital-library.theiet.org/files/IET_IPR_CFP_EC.pdf AI-Powered 3D Vision - https://digital-library.theiet.org/files/IET_IPR_CFP_AIPV.pdf Multidisciplinary advancement of Imaging Technologies: From Medical Diagnostics and Genomics to Cognitive Machine Vision, and Artificial Intelligence - https://digital-library.theiet.org/files/IET_IPR_CFP_IST.pdf Deep Learning for 3D Reconstruction - https://digital-library.theiet.org/files/IET_IPR_CFP_DLR.pdf
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