Robust Document Image Forgery Localization Against Image Blending

Digital documents, as a twin of hard copy, are increasingly being used as credible evidence. Unfortunately, digital document images easily suffer forgery or malicious manipulation, with the availability of sophisticated image editing tools. To verify and detect the possible forgeries for a given document, a number of forensic schemes have been developed. However, in the realworld scenario, the doctored image could be further processed or transmitted over a channel with unknown distortion, which dramatically degrade the forgery detection performance. In this work, we make the first step towards designing a robust document image forgery localization against image blending. Specifically, we propose an encoder-decoder neural network architecture consisting of three modules. The first module is responsible for capturing the multi-scale features from the high-level feature maps, and the remaining two attention-based modules aim to extract lowlevel local features and high-level global features. For training the model, we construct a dedicated forgery document database processed by several recent image blending procedures. Extensive experiments demonstrate the effectiveness and superiority of the proposed method in detecting the forgery that undergoes image blending. The source code, models and the constructed image dataset are publicly available at https://github.com/lwp0201/ Image- Forgery- Localization-Against-Image-Blending.

Automated summary

Digital documents are increasingly being used as credible evidence, but they are vulnerable to forgery and manipulation. This study proposes a robust method for detecting document image forgery, using a neural network architecture and attention mechanisms to extract features and identify forgeries.