Official implementation of TTFN and the RWTamper dataset from the paper "Bridging the Reality Gap in Tampered Text Detection: A Human-Crafted Real-World Dataset and a Text-Centric Approach" (IEEE TIFS 2026).
The official implementation of TTFN is currently being prepared.
We plan to release the source code, training scripts, and evaluation pipeline in this repository in a future update.
Please stay tuned.
The RWTamper dataset is publicly available at ModelScope.
Note:
- The RWTamper dataset is available for non-commercial research purposes only. Scholars or organizations interested in using the dataset may submit an application through our online platform:
- We will give you the decompression password after your application has been received and approved.
- The original data of the dataset is sourced from public channels such as the Internet, and its copyright shall remain with the original providers. The collated and annotated dataset presented in this case is for non-commercial use only and is currently licensed to universities and research institutions. To apply for the use of this dataset, please fill in the corresponding application form in accordance with the requirements specified on the dataset’s official website. The applicant must be a full-time employee of a university or research institute and is required to sign the application form. For the convenience of review, it is recommended to affix an official seal (a seal of a secondary-level department is acceptable).
- All users must follow all use conditions; otherwise, the authorization will be revoked.
The code and dataset should be used and distributed under (CC BY-NC-ND 4.0) for non-commercial research purposes.
If you have any questions, feel free to contact me at eegtxu@mail.scut.edu.cn.
- This repository can only be used for non-commercial research purposes.
- For commercial use, please contact Prof. Lianwen Jin (eelwjin@scut.edu.cn).
- Copyright 2025, Deep Learning and Vision Computing Lab (DLVC-Lab), South China University of Technology.
If you find this paper helpful, please consider giving this repo a ⭐ and citing:
@ARTICLE{11570917,
author={Xu, Guitao and Zhang, Peirong and Jin, Lianwen},
journal={IEEE Transactions on Information Forensics and Security},
title={Bridging the Reality Gap in Tampered Text Detection: A Human-Crafted Real-World Dataset and a Text-Centric Approach},
year={2026},
volume={21},
number={},
pages={6498-6513}}