Image Generation and Learning Strategy for Deep Document Forgery
  Detection
Yamato Okamoto Osada Genki Iu Yahiro Rintaro Hasegawa Peifei Zhu Hirokatsu Kataoka
In recent years, document processing has flourished and brought numerous
benefits. However, there has been a significant rise in reported cases of
forged document images. Specifically, recent advancements in deep neural
network (DNN) methods for generative tasks may amplify the threat of document
forgery. Traditional approaches for forged document images created by prevalent
copy-move methods are unsuitable against those created by DNN-based methods, as
we have verified. To address this issue, we construct a training dataset of
document forgery images, named FD-VIED, by emulating possible attacks, such as
text addition, removal, and replacement with recent DNN-methods. Additionally,
we introduce an effective pre-training approach through self-supervised
learning with both natural images and document images. In our experiments, we
demonstrate that our approach enhances detection performance.