CTP-Net：用于文档图像伪造定位的字符纹理感知网络
CTP-Net: Character Texture Perception Network for Document Image Forgery Localization
AI总结：
本文提出融合字符纹理流与图像纹理流的 CTP-Net，并构建 FCTM 数据集，以在后处理条件下精准定位文档图像的多尺度伪造区域。
摘要：
近年来，随着信息技术的发展，文档图像在社交网络上得到广泛传播。借助强大的图像编辑工具，文档图像很容易被伪造且不会留下可见的操作痕迹；如果重要信息被恶意篡改使用，将引发严重问题。因此，文档图像取证研究值得进一步探索。本文提出字符纹理感知网络（Character Texture Perception Network，CTP-Net），用于定位文档图像中的伪造区域。具体而言，考虑到文档图像中具有语义的字符极易受到攻击，捕捉伪造痕迹是定位伪造区域的关键。我们设计了基于光学字符识别的字符纹理流（Character Texture Stream，CTS），以捕捉文本区域的特征，而文本区域是文档图像的重要组成部分。同时，图像纹理流（Image Texture Stream，ITS）用于提取整幅文档图像的纹理特征。通过结合 CTS 与 ITS 提取的特征，CTP-Net 能够揭示文档图像中更细微的伪造痕迹。此外，为克服伪造文档图像不足带来的挑战，我们设计了一种数据生成策略，用于构建 Fake Chinese Trademark 数据集（FCTM）。在不同数据集上的实验结果表明，所提出的 CTP-Net 能够定位文档图像中的多尺度伪造区域，并且即使在应用后处理操作的情况下，仍优于最先进的伪造定位方法。
英文摘要：
Due to the progression of information technology in recent years, document images have been widely disseminated on social networks. With the help of powerful image editing tools, document images are easily forged without leaving visible manipulation traces, which leads to severe issues if significant information is falsified for malicious use. Therefore, the research of document image forensics is worth further exploring. In this paper, we propose a Character Texture Perception Network (CTP-Net) to localize the forged regions in document images. Specifically, considering the characters with semantics in a document image are highly vulnerable, capturing the forgery traces is the key to localize the forged regions. We design a Character Texture Stream (CTS) based on optical character recognition to capture features of text areas that are essential components of a document image. Meanwhile, texture features of the whole document image are exploited by an Image Texture Stream (ITS). Combining the features extracted from the CTS and the ITS, the CTP-Net can reveal more subtle forgery traces from document images. Moreover, to overcome the challenge caused by the lack of fake document images, we design a data generation strategy that is utilized to construct a Fake Chinese Trademark dataset (FCTM). Experimental results on different datasets demonstrate that the proposed CTP-Net is able to localize multi-scale forged areas in document images, and outperform the state-of-the-art forgery localization methods, even though post-processing operations are applied.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
