# Deep Learning-based Forgery Attack on Document Images

arXiv: https://arxiv.org/abs/2102.00653 · HTML: https://mlsanity.com/paper/2102.00653/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2021.

## Built on by (most cited first, 14 of 14)

- [Exploring Stroke-Level Modifications for Scene Text Editing](https://mlsanity.com/paper/2212.01982/) (2022)
- [Toward Real Text Manipulation Detection: New Dataset and New Solution](https://mlsanity.com/paper/2312.06934/) (2023)
- [Visual Text Processing: A Comprehensive Review and Unified Evaluation](https://mlsanity.com/paper/2504.21682/) (2025)
- [HateProof: Are Hateful Meme Detection Systems really Robust?](https://mlsanity.com/paper/2302.05703/) (2023)
- [FASTER: A Font-Agnostic Scene Text Editing and Rendering Framework](https://mlsanity.com/paper/2308.02905/) (2023)
- [Identity Card Presentation Attack Detection: A Systematic Review](https://mlsanity.com/paper/2511.06056/) (2025)
- [Domain Generalized Recaptured Screen Image Identification Using SWIN Transformer](https://mlsanity.com/paper/2407.17170/) (2024)
- [EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents](https://mlsanity.com/paper/2508.16284/) (2025)
- [From Forgeries to Foundation Models: A Systematic Survey of Identity Document Attack and Detection](https://mlsanity.com/paper/2607.01442/) (2026)
- [Natural Scene Text Editing Based on AI](https://mlsanity.com/paper/2111.15475/) (2021)
- [Forensicability Assessment of Questioned Images in Recapturing Detection](https://mlsanity.com/paper/2209.01935/) (2022)
- [Object-fabrication Targeted Attack for Object Detection](https://mlsanity.com/paper/2212.06431/) (2022)
- [CTP-Net: Character Texture Perception Network for Document Image Forgery Localization](https://mlsanity.com/paper/2308.02158/) (2023)
- [TripleFDS: Triple Feature Disentanglement and Synthesis for Scene Text Editing](https://mlsanity.com/paper/2511.13399/) (2025)

## Abstract (arXiv)

With the ongoing popularization of online services, the digital document images have been used in various applications. Meanwhile, there have emerged some deep learning-based text editing algorithms which alter the textual information of an image . In this work, we present a document forgery algorithm to edit practical document images. To achieve this goal, the limitations of existing text editing algorithms towards complicated characters and complex background are addressed by a set of network design strategies. First, the unnecessary confusion in the supervision data is avoided by disentangling the textual and background information in the source images. Second, to capture the structure of some complicated components, the text skeleton is provided as auxiliary information and the continuity in texture is considered explicitly in the loss function. Third, the forgery traces induced by the text editing operation are mitigated by some post-processing operations which consider the distortions from the print-and-scan channel. Quantitative comparisons of the proposed method and the exiting approach have shown the advantages of our design by reducing the about 2/3 reconstruction error measured in MSE, improving reconstruction quality measured in PSNR and in SSIM by 4 dB and 0.21, respectively. Qualitative experiments have confirmed that the reconstruction results of the proposed method are visually better than the existing approach. More importantly, we have demonstrated the performance of the proposed document forgery algorithm under a practical scenario where an attacker is able to alter the textual information in an identity document using only one sample in the target domain. The forged-and-recaptured samples created by the proposed text editing attack and recapturing operation have successfully fooled some existing document authentication systems.