Due to the progression of information technology in recent years, document images have been widely disseminated in 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 a document image, the character with specific semantic information is most vulnerable to tampering, for which capturing the forgery traces of the character is the key to localizing the forged region in document images. Considering both character and image textures, in this paper, we propose a Character Texture Perception Network (CTP-Net) to localize the forgery of document images. Based on optical character recognition, a Character Texture Stream (CTS) is designed 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. 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). Through a series of experiments, we show that the proposed CTP-Net is able to capture tampering traces in document images, especially in text regions. Experimental results demonstrate that CTP-Net can localize multi-scale forged areas in document images and outperform the state-of-the-art forgery localization methods.

翻译：近年来，随着信息技术的进步，文档图像在社交网络中广泛传播。借助强大的图像编辑工具，文档图像易于被伪造且不留下可见的篡改痕迹，若重要信息被恶意篡改将引发严重问题。因此，文档图像取证研究值得进一步探索。在文档图像中，具有特定语义信息的字符最易遭受篡改，捕捉字符的伪造痕迹是定位文档图像伪造区域的关键。本文综合考虑字符与图像纹理，提出一种字符纹理感知网络（CTP-Net）用于文档图像伪造定位。基于光学字符识别，我们设计了字符纹理流（CTS）以捕捉文档图像核心组成部分——文本区域的特征。同时，通过图像纹理流（ITS）提取整个文档图像的纹理特征。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。为克服伪造文档图像不足的挑战，我们设计了一种数据生成策略，并据此构建了伪造中文商标数据集（FCTM）。通过系列实验表明，所提出的CTP-Net能够捕捉文档图像（尤其是文本区域）中的篡改痕迹。实验结果显示，CTP-Net可定位文档图像中多尺度的伪造区域，并优于现有最先进的伪造定位方法。