Image Generation and Learning Strategy for Deep Document Forgery Detection
This addresses the threat of document forgery amplified by DNN advancements, though it appears incremental as it builds on existing detection methods with new data and training strategies.
The paper tackles the problem of detecting forged document images created by deep neural network methods, which traditional approaches fail against, by constructing a training dataset (FD-VIED) and introducing a self-supervised pre-training approach, resulting in enhanced detection performance.
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.