# Image Generation and Learning Strategy for Deep Document Forgery Detection

arXiv: https://arxiv.org/abs/2311.03650 · HTML: https://mlsanity.com/paper/2311.03650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Large Batch Training of Convolutional Networks](https://mlsanity.com/paper/1708.03888/) (2017)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [Synthetic Data for Text Localisation in Natural Images](https://mlsanity.com/paper/1604.06646/) (2016)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [DocFormer: End-to-End Transformer for Document Understanding](https://mlsanity.com/paper/2106.11539/) (2021)
- [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://mlsanity.com/paper/1502.07058/) (2015)
- [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020)
- [Unifying Vision, Text, and Layout for Universal Document Processing](https://mlsanity.com/paper/2212.02623/) (2022)
- [DeepFaceLab: Integrated, flexible and extensible face-swapping framework](https://mlsanity.com/paper/2005.05535/) (2020)
- [Two-Stream Neural Networks for Tampered Face Detection](https://mlsanity.com/paper/1803.11276/) (2018)
- [STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing](https://mlsanity.com/paper/1904.09709/) (2019)
- [Image Manipulation Detection by Multi-View Multi-Scale Supervision](https://mlsanity.com/paper/2104.06832/) (2021)
- [Aggregated Contextual Transformations for High-Resolution Image Inpainting](https://mlsanity.com/paper/2104.01431/) (2021)
- [Editing Text in the Wild](https://mlsanity.com/paper/1908.03047/) (2019)
- [Exploring Stroke-Level Modifications for Scene Text Editing](https://mlsanity.com/paper/2212.01982/) (2022)
- [De-rendering Stylized Texts](https://mlsanity.com/paper/2110.01890/) (2021)
- [Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification](https://mlsanity.com/paper/1704.03557/) (2017)
- [Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation](https://mlsanity.com/paper/2210.13129/) (2022)
- [RewriteNet: Reliable Scene Text Editing with Implicit Decomposition of Text Contents and Styles](https://mlsanity.com/paper/2107.11041/) (2021)

## Built on by (most cited first, 1 of 1)

- [Can Multi-modal (reasoning) LLMs detect document manipulation?](https://mlsanity.com/paper/2508.11021/) (2025)

## Abstract (arXiv)

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.