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.

基于字符纹理感知网络（CTP-Net），本文提出了一种在文档图像中定位伪造的方法，通过捕捉字符的伪造痕迹，结合整个文档图像的纹理特征，能够更准确地定位文档图像中的伪造区域，并且在实验证明了该方法优于现有的伪造定位方法。