Image Generation and Learning Strategy for Deep Document Forgery Detection

• URL: http://arxiv.org/abs/2311.03650v1
• Date: Tue, 7 Nov 2023 01:40:00 GMT
• Title: Image Generation and Learning Strategy for Deep Document Forgery Detection
• Authors: Yamato Okamoto, Osada Genki, Iu Yahiro, Rintaro Hasegawa, Peifei Zhu, Hirokatsu Kataoka
• Abstract summary: Recent advancements in deep neural network (DNN) methods for generative tasks may amplify the threat of document forgery. We construct a training dataset of document forgery images, named FD-VIED, by emulating possible attacks. In our experiments, we demonstrate that our approach enhances detection performance.
• Score: 7.585489507445007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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