# Toward Real Text Manipulation Detection: New Dataset and New Solution

arXiv: https://arxiv.org/abs/2312.06934 · HTML: https://mlsanity.com/paper/2312.06934/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 58 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019)
- [ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction](https://mlsanity.com/paper/2103.10213/) (2021)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [Neighborhood Attention Transformer](https://mlsanity.com/paper/2204.07143/) (2022)
- [Real-time Scene Text Detection with Differentiable Binarization](https://mlsanity.com/paper/1911.08947/) (2019)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection](https://mlsanity.com/paper/2112.08935/) (2021)
- [Image Manipulation Detection by Multi-View Multi-Scale Supervision](https://mlsanity.com/paper/2104.06832/) (2021)
- [Editing Text in the Wild](https://mlsanity.com/paper/1908.03047/) (2019)
- [Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2108.12947/) (2021)
- [ObjectFormer for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2203.14681/) (2022)
- [Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation](https://mlsanity.com/paper/1905.05980/) (2019)
- [Deep Learning-based Forgery Attack on Document Images](https://mlsanity.com/paper/2102.00653/) (2021)
- [TNCR: Table Net Detection and Classification Dataset](https://mlsanity.com/paper/2106.15322/) (2021)

## Built on by (most cited first, 20 of 20)

- [ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization](https://mlsanity.com/paper/2505.11003/) (2025)
- [UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization](https://mlsanity.com/paper/2510.03161/) (2025)
- [Venus-DeFakerOne: Unified Fake Image Detection & Localization](https://mlsanity.com/paper/2605.14091/) (2026)
- [AIForge-Doc: A Benchmark for Detecting AI-Forged Tampering in Financial and Form Documents](https://mlsanity.com/paper/2602.20569/) (2026)
- [DOCFORGE-BENCH: A Comprehensive 0-shot Benchmark for Document Forgery Detection and Analysis](https://mlsanity.com/paper/2603.01433/) (2026)
- [MonkeyOCRv2: A Visual-Text Foundation Model for Document AI](https://mlsanity.com/paper/2607.11562/) (2026)
- [Webly-Supervised Image Manipulation Localization via Category-Aware Auto-Annotation](https://mlsanity.com/paper/2508.20987/) (2025)
- [Revisiting Image Manipulation Localization under Realistic Manipulation Scenarios](https://mlsanity.com/paper/2509.20006/) (2025)
- [Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction](https://mlsanity.com/paper/2602.06676/) (2026)
- [When the Forger Is the Judge: GPT-Image-2 Cannot Recognize Its Own Faked Documents](https://mlsanity.com/paper/2604.25213/) (2026)
- [Multi-stage Prompt Refinement for Mitigating Hallucinations in Large Language Models](https://mlsanity.com/paper/2510.12032/) (2025)
- [Leveraging Contrastive Learning for a Similarity-Guided Tampered Document Data Generation Pipeline](https://mlsanity.com/paper/2602.17322/) (2026)
- [SEED: Simple ViT and Evolving Harness for Explainable Text Forgery Detection](https://mlsanity.com/paper/2606.21138/) (2026)
- [Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems](https://mlsanity.com/paper/2507.21157/) (2025)
- [DocQT: Improving Document Forgery Localization Robustness via Diverse JPEG Quantization Tables](https://mlsanity.com/paper/2605.19688/) (2026)
- [Efficient Document Tampering Localization with Multi-Level Discrepancy Features and Unified DCT-Quantization Embedding](https://mlsanity.com/paper/2606.22285/) (2026)
- [Open-Set Visual Text Forensics via Sparse-Constraint Rectified Flow](https://mlsanity.com/paper/2608.02258/) (2026)
- [Evidence-Guided Detection, Localization and Explanation for Text-Centric Image Forensics](https://mlsanity.com/paper/2609.02097/) (2026)
- [Beyond Natural Images: Rethinking AI-Generated Image Detection in Documents](https://mlsanity.com/paper/2609.14352/) (2026)
- [From Sharp Eyes to Expert Mind: Internalizing Expert Knowledge in MLLMs for Tampered Text Detection](https://mlsanity.com/paper/2609.36145/) (2026)

## Abstract (arXiv)

With the surge in realistic text tampering, detecting fraudulent text in images has gained prominence for maintaining information security. However, the high costs associated with professional text manipulation and annotation limit the availability of real-world datasets, with most relying on synthetic tampering, which inadequately replicates real-world tampering attributes. To address this issue, we present the Real Text Manipulation (RTM) dataset, encompassing 14,250 text images, which include 5,986 manually and 5,258 automatically tampered images, created using a variety of techniques, alongside 3,006 unaltered text images for evaluating solution stability. Our evaluations indicate that existing methods falter in text forgery detection on the RTM dataset. We propose a robust baseline solution featuring a Consistency-aware Aggregation Hub and a Gated Cross Neighborhood-attention Fusion module for efficient multi-modal information fusion, supplemented by a Tampered-Authentic Contrastive Learning module during training, enriching feature representation distinction. This framework, extendable to other dual-stream architectures, demonstrated notable localization performance improvements of 7.33% and 6.38% on manual and overall manipulations, respectively. Our contributions aim to propel advancements in real-world text tampering detection. Code and dataset will be made available at https://github.com/DrLuo/RTM