文本图像篡改检测与定位综述

句福娇; 张浩; 齐光磊; 王宏远

doi:10.12263/DZXB.20250830

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文本图像篡改检测与定位综述

综述评论 | 更新时间：2026-06-16

- 文本图像篡改检测与定位综述
- A Comprehensive Review of Text Image Tampering Detection and Localization
- 电子学报 2026年54卷第3期页码：1364-1390
- 作者机构：
  
  1.北京工业大学计算机学院，北京 102101
  2.北京邮电大学世纪学院计算机科学与技术系，北京 102101
- 作者简介：
  
  句福娇女，1987年出生。现为北京工业大学计算机学院副教授、博士生导师。主要研究领域为深度学习，计算机视觉。E-mail: jfj2017@bjut.edu.cn
  张浩男，2000年出生。现为北京工业大学计算机学院硕士研究生。主要研究领域为深度学习、计算机视觉。E-mail: zh2024@emails.bjut.edu.cn
  齐光磊男，1979年出生。现为北京邮电大学世纪学院计算机科学与技术系副教授。主要研究领域为深度学习、计算机视觉。E-mail: qiguanglei@ccbupt.cn
  王宏远女，1988年出生。现为北京工业大学计算机学院讲师。主要研究领域为信息安全、大数据安全。E-mail: wanghongyuan@bjut.edu.cn
- 基金信息：
  
  北京市自然科学基金(4242016;4244072)
- DOI：10.12263/DZXB.20250830
  中图分类号： TP391;
- 收稿：2025-09-23，
  
  录用：2026-02-28，
  
  纸质出版：2026-03-25
- 稿件说明：
移动端阅览
句福娇, 张浩, 齐光磊, 等. 文本图像篡改检测与定位综述[J]. 电子学报, 2026, 54(03): 1364-1390.

JU Fujiao, ZHANG Hao, QI Guanglei, et al. A Comprehensive Review of Text Image Tampering Detection and Localization[J]. Acta Electronica Sinica, 2026, 54(03): 1364-1390.
句福娇, 张浩, 齐光磊, 等. 文本图像篡改检测与定位综述[J]. 电子学报, 2026, 54(03): 1364-1390. DOI：10.12263/DZXB.20250830

JU Fujiao, ZHANG Hao, QI Guanglei, et al. A Comprehensive Review of Text Image Tampering Detection and Localization[J]. Acta Electronica Sinica, 2026, 54(03): 1364-1390. DOI：10.12263/DZXB.20250830

摘要

随着生成式人工智能技术的快速发展，文本图像的篡改手段日趋智能和隐蔽，严重威胁学术诚信、信息安全与社会信任。文本图像篡改分析（检测与定位）旨在判别图像是否存在篡改，并进一步定位图像中被篡改的文本区域，以维护信息的真实性和图像的可信度。本文系统回顾了近年来该领域的研究进展，从单流视觉建模、多模态融合检测、文本语义与结构一致性分析三个视角梳理了现有的深度学习篡改分析方法，并分析各类方法的设计思路与适用场景。在此基础上，本文进一步从模型鲁棒性与工程部署两个横向维度，重点讨论了近年来出现的前沿技术，包括对抗样本训练策略、大型视觉语言预训练模型在文本一致性判定中的应用、跨语种与场景文本检测的挑战、面向嵌入式系统以实现高效部署的轻量化检测网络，以及融合语言模型生成解释以增强模型透明度和用户信任的可解释性方法。在评估基准方面，本文总结了现有公开数据集及其规模和特征，并对代表性方法的检测与定位性能和模型复杂度进行对比分析。最后，结合现有研究工作，本文提出了有待解决的难点与未来发展趋势，为文本图像篡改检测与定位领域提供了全面的技术视角和研究参考。

Abstract

With the rapid development of generative artificial intelligence

text images can be tampered with in increasingly subtle and realistic ways

posing severe threats to academic integrity

information security

and social trust. Text image tampering analysis

covering both tampering detection (image-level authenticity judgement) and tampering localization (pixel-level delineation of manipulated text regions)

aims to verify image authenticity and provide fine-grained evidence for downstream forensics. This paper systematically reviews recent progress in this field and organizes deep learning-based methods from three perspectives: single-stream visual modeling for mining forensic traces

multimodal fusion for integrating complementary cues (e.g.

spatial

frequency

and degradation artifacts)

and semantic/structural consistency analysis for exploiting textual content and layout constraints. Beyond these methodological routes

we further highlight two cross-cutting dimensions that have gained momentum in recent years

namely robustness improvement under adversarial perturbations and real-world corruptions

and practical deployment including lightweight architectures and explainable outputs to enhance efficiency and user trust. We also discuss the emerging role of large pre-trained vision-language models (VLMs) in text consistency verification

as well as challenges in cross-language settings and in-the-wild scene text. For evaluation

we summarize publicly available datasets and commonly used metrics

and compare representative methods in terms of detection/localization performance and model complexity. Finally

we outline open problems and future research directions to facilitate further advances in text image tampering detection and localization.

关键词

Keywords

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