A Comprehensive Review of Text Image Tampering Detection and Localization

JU Fujiao; ZHANG Hao; QI Guanglei; WANG Hongyuan

doi:10.12263/DZXB.20250830

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A Comprehensive Review of Text Image Tampering Detection and Localization

SURVEYS AND REVIEWS | 更新时间：2026-06-26

- A Comprehensive Review of Text Image Tampering Detection and Localization
- ACTA ELECTRONICA SINICA Vol. 54, Issue 3, Pages: 1364-1390(2026)
- 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：
- 基金信息：
  
  Beijing Natural Science Foundation(4242016;4244072)
- DOI：10.12263/DZXB.20250830
  CLC： TP391;
- Received：23 September 2025，
  
  Accepted：28 February 2026，
  
  Published：25 March 2026
- 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

references

Roy P , Bag S . Detection of handwritten document forgery by analyzing writers’ handwritings [C ] // Pattern Recognition and Machine Intelligence . Cham : Springer , 2019 : 596 - 605 . DOI: 10.1007/978-3-030-34869-4_65 http://dx.doi.org/10.1007/978-3-030-34869-4_65

Verdoliva L . Media forensics and DeepFakes: An overview [J ] . IEEE Journal of Selected Topics in Signal Processing , 2020 , 14 ( 5 ): 910 - 932 . DOI: 10.1109/JSTSP.2020.3002101 http://dx.doi.org/10.1109/JSTSP.2020.3002101

王裕鑫 , 张博强 , 谢洪涛 , 等 . 基于空域与频域关系建模的篡改文本图像检测 [J ] . 网络与信息安全学报 , 2022 , 8 ( 3 ): 29 - 40 . DOI: 10.11959/j.issn.2096-109x.2022035 http://dx.doi.org/10.11959/j.issn.2096-109x.2022035

Wang Yuxin , Zhang Boqiang , Xie Hongtao , et al . Tampered text detection via RGB and frequency relationship modeling [J ] . Chinese Journal of Network and Information Security , 2022 , 8 ( 3 ): 29 - 40 . (in Chinese) . DOI: 10.11959/j.issn.2096-109x.2022035 http://dx.doi.org/10.11959/j.issn.2096-109x.2022035

Zhao Lin , Chen Changsheng , Huang Jiwu . Deep learning-based forgery attack on document images [J ] . IEEE Transactions on Image Processing , 2021 , 30 : 7964 - 7979 . DOI: 10.1109/tip.2021.3112048 http://dx.doi.org/10.1109/tip.2021.3112048

Luo Dongliang , Liu Yuliang , Yang Rui , et al . Toward real text manipulation detection: New dataset and new solution [J ] . Pattern Recognition , 2025 , 157 : 110828 . DOI: 10.1016/j.patcog.2024.110828 http://dx.doi.org/10.1016/j.patcog.2024.110828

Lampert C H , Mei Lin , Breuel T M . Printing technique classification for document counterfeit detection [C ] // 2006 International Conference on Computational Intelligence and Security . Piscataway : IEEE , 2006 : 639 - 644 . DOI: 10.1109/iccias.2006.294214 http://dx.doi.org/10.1109/iccias.2006.294214

Zhou Peng , Han Xintong , Morariu V I , et al . Learning rich features for image manipulation detection [C ] // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 1053 - 1061 . DOI: 10.1109/CVPR.2018.00116 http://dx.doi.org/10.1109/CVPR.2018.00116

Bertrand R , Terrades O R , Gomez-Krämer P , et al . A conditional random field model for font forgery detection [C ] // 2015 13th International Conference on Document Analysis and Recognition . Piscataway : IEEE , 2015 : 576 - 580 . DOI: 10.1109/icdar.2015.7333827 http://dx.doi.org/10.1109/icdar.2015.7333827

Van Beusekom J , Shafait F , Breuel T M . Text-line examination for document forgery detection [J ] . International Journal on Document Analysis and Recognition (IJDAR) , 2013 , 16 ( 2 ): 189 - 207 . DOI: 10.1007/s10032-011-0181-5 http://dx.doi.org/10.1007/s10032-011-0181-5

Shang Shize , Kong Xiangwei , You Xingang . Document forgery detection using distortion mutation of geometric parameters in characters [J ] . Journal of Electronic Imaging , 2015 , 24 ( 2 ): 023008 . DOI: 10.1117/1.JEI.24.2.023008 http://dx.doi.org/10.1117/1.JEI.24.2.023008

Ryu S J , Lee H Y , Cho I W , et al . Document forgery detection with SVM classifier and image quality measures [M ] // Advances in multimedia information processing - PCM 2008 . Berlin, HeidelbergSpringer, 2008 : 486 - 495 . DOI: 10.1007/978-3-540-89796-5_50 http://dx.doi.org/10.1007/978-3-540-89796-5_50

Lin Zhouchen , He Junfeng , Tang Xiaoou , et al . Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis [J ] . Pattern Recognition , 2009 , 42 ( 11 ): 2492 - 2501 . DOI: 10.1016/j.patcog.2009.03.019 http://dx.doi.org/10.1016/j.patcog.2009.03.019

Cruz F , Sidère N , Coustaty M , et al . Local binary patterns for document forgery detection [C ] // 2017 14th IAPR International Conference on Document Analysis and Recognition . Piscataway : IEEE , 2017 : 1223 - 1228 . DOI: 10.1109/icdar.2017.202 http://dx.doi.org/10.1109/icdar.2017.202

Liang Weipeng , Dong Li , Wang Rangding , et al . Robust document image forgery localization against image blending [C ] // 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications . Piscataway : IEEE , 2022 : 810 - 817 . DOI: 10.1109/trustcom56396.2022.00113 http://dx.doi.org/10.1109/trustcom56396.2022.00113

Bi Xiuli , Wei Yang , Xiao Bin , et al . RRU-Net: The ringed residual U-Net for image splicing forgery detection [C ] // 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . Piscataway : IEEE , 2019 : 30 - 39 . DOI: 10.1109/CVPRW.2019.00010 http://dx.doi.org/10.1109/CVPRW.2019.00010

Chen Xinru , Dong Chengbo , Ji Jiaqi , et al . Image manipulation detection by multi-view multi-scale supervision [C ] // 2021 IEEE/CVF International Conference on Computer Vision . Piscataway : IEEE , 2021 : 14165 - 14173 . DOI: 10.48550/arXiv.2104.06832 http://dx.doi.org/10.48550/arXiv.2104.06832

Ronneberger O , Fischer P , Brox T . U-Net: Convolutional networks for biomedical image segmentation [M ] // Medical image computing and computer-assisted intervention - MICCAI 2015 . ChamSpringer International Publishing , 2015 : 234 - 241 . DOI: 10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28

Islam A , Long Chengjiang , Basharat A , et al . DOA-GAN: Dual-order attentive generative adversarial network for image copy-move forgery detection and localization [C ] // 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2020 : 4675 - 4684 . DOI: 10.1109/CVPR42600.2020.00473 http://dx.doi.org/10.1109/CVPR42600.2020.00473

Zhuang Peiyu , Li Haodong , Tan Shunquan , et al . Image tampering localization using a dense fully convolutional network [J ] . IEEE Transactions on Information Forensics and Security , 2021 , 16 : 2986 - 2999 . DOI: 10.1109/TIFS.2021.3070444 http://dx.doi.org/10.1109/TIFS.2021.3070444

Zhang Yulan , Zhu Guopu , Wu Ligang , et al . Multi-task SE-network for image splicing localization [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2022 , 32 ( 7 ): 4828 - 4840 . DOI: 10.1109/tcsvt.2021.3123829 http://dx.doi.org/10.1109/tcsvt.2021.3123829

Roy P , Bhattacharya S , Ghosh S , et al . STEFANN: Scene text editor using font adaptive neural network [C ] // 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2020 : 13225 - 13234 . DOI: 10.1109/CVPR42600.2020.01324 http://dx.doi.org/10.1109/CVPR42600.2020.01324

Pérez P , Gangnet M , Blake A . Poisson image editing [C ] // ACM SIGGRAPH 2003 Papers . New York : ACM , 2003 : 313 - 318 . DOI: 10.1145/1201775.882269 http://dx.doi.org/10.1145/1201775.882269

Wu Liang , Zhang Chengquan , Liu Jiaming , et al . Editing text in the wild [C ] // Proceedings of the 27th ACM International Conference on Multimedia . New York : ACM , 2019 : 1500 - 1508 . DOI: 10.1145/3343031.3350929 http://dx.doi.org/10.1145/3343031.3350929

Yang Qiangpeng , Huang Jun , Lin Wei . SwapText: Image based texts transfer in scenes [C ] // 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2020 : 14688 - 14697 . DOI: 10.1109/CVPR42600.2020.01471 http://dx.doi.org/10.1109/CVPR42600.2020.01471

Chai Shang , Zhuang Liansheng , Yan Fengying . LayoutDM: Transformer-based diffusion model for layout generation [C ] // 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2023 : 18349 - 18358 . DOI: 10.48550/arXiv.2305.02567 http://dx.doi.org/10.48550/arXiv.2305.02567

Diaz M , Mendoza-García A , Ferrer M A , et al . A survey of handwriting synthesis from 2019 to 2024: A comprehensive review [J ] . Pattern Recognition , 2025 , 162 : 111357 . DOI: 10.1016/j.patcog.2025.111357 http://dx.doi.org/10.1016/j.patcog.2025.111357

Artaud C , Sidère N , Doucet A , et al . Find it! Fraud detection contest report [C ] // 2018 24th International Conference on Pattern Recognition . Piscataway : IEEE , 2018 : 13 - 18 . DOI: 10.1109/icpr.2018.8545428 http://dx.doi.org/10.1109/icpr.2018.8545428

Alibaba Cloud Comput . Softw . Co. Security AI Challenger Program[EB/OL ] . (2020) . https://tianchi.aliyun.com/competition/entrance/531812/introduction https://tianchi.aliyun.com/competition/entrance/531812/introduction . DOI: 10.1007/978-981-15-7749-9_10 http://dx.doi.org/10.1007/978-981-15-7749-9_10

Xu Wenbo , Luo Junwei , Zhu Chuntao , et al . Document images forgery localization using a two-stream network [J ] . International Journal of Intelligent Systems , 2022 , 37 ( 8 ): 5272 - 5289 . DOI: 10.1002/int.22792 http://dx.doi.org/10.1002/int.22792

Wang Yuxin , Xie Hongtao , Xing Mengting , et al . Detecting tampered scene text in the wild [M ] // Computer Vision - ECCV 2022 . ChamSpringer Nature Switzerland, 2022 : 215 - 232 . DOI: 10.1007/978-3-031-19815-1_13 http://dx.doi.org/10.1007/978-3-031-19815-1_13

Alibaba Cloud Comput . Softw . Co. Real-World Image Forgery Localization Challenge[EB/OL ] . (2022) . https://tianchi.aliyun.com/competition/entrance/531945/introduction https://tianchi.aliyun.com/competition/entrance/531945/introduction . DOI: 10.1007/978-981-15-7749-9_10 http://dx.doi.org/10.1007/978-981-15-7749-9_10

Alibaba Cloud Comput . Softw . Co. Detecting Tampered Text in Images Tianchi Competition[EB/OL ] . (2023) . https://tianchi.aliyun.com/competition/entrance/532052/introduction https://tianchi.aliyun.com/competition/entrance/532052/introduction . DOI: 10.1007/978-981-15-7749-9_10 http://dx.doi.org/10.1007/978-981-15-7749-9_10

Qu Chenfan , Liu Chongyu , Liu Yuliang , et al . Towards robust tampered text detection in document image: New dataset and new solution [C ] // 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2023 : 5937 - 5946 . DOI: 10.1109/cvpr52729.2023.00575 http://dx.doi.org/10.1109/cvpr52729.2023.00575

Tornés B M , Taburet T , Boros E , et al . Receipt dataset for document forgery detection [M ] // Document analysis and recognition - ICDAR 2023 . ChamSpringer Nature Switzerland, 2023 : 454 - 469 . DOI: 10.1007/978-3-031-41682-8_28 http://dx.doi.org/10.1007/978-3-031-41682-8_28

Dong Li , Liang Weipeng , Wang Rangding . Robust text image tampering localization via forgery traces enhancement and multiscale attention [J ] . IEEE Transactions on Consumer Electronics , 2024 , 70 ( 1 ): 3495 - 3507 . DOI: 10.1109/TCE.2024.3367947 http://dx.doi.org/10.1109/TCE.2024.3367947

张汝波 , 蔺庆龙 , 张天一 . 基于深度学习的图像篡改检测方法综述 [J ] . 智能系统学报 , 2025 , 20 ( 2 ): 283 - 304 .

Zhang Rubo , Lin Qinglong , Zhang Tianyi . A review of image tampering detection methods based on deep learning [J ] . CAAI Transactions on Intelligent Systems , 2025 , 20 ( 2 ): 283 - 304 . (in Chinese)

Zhang Lingzhi , Wen T , Shi Jianbo . Deep image blending [C ] // 2020 IEEE Winter Conference on Applications of Computer Vision . Piscataway : IEEE , 2020 : 231 - 240 . DOI: 10.1109/WACV45572.2020.9093632 http://dx.doi.org/10.1109/WACV45572.2020.9093632

Sun Yu , Ni Rongrong , Zhao Yao . MFAN: Multi-level features attention network for fake certificate image detection [J ] . Entropy , 2022 , 24 ( 1 ): 118 . DOI: 10.3390/e24010118 http://dx.doi.org/10.3390/e24010118

Ferrara P , Bianchi T , De Rosa A , et al . Image forgery localization via fine-grained analysis of CFA artifacts [J ] . IEEE Transactions on Information Forensics and Security , 2012 , 7 ( 5 ): 1566 - 1577 . DOI: 10.1109/tifs.2012.2202227 http://dx.doi.org/10.1109/tifs.2012.2202227

Wu Yue , AbdAlmageed W , Natarajan P . ManTra-net: Manipulation tracing network for detection and localization of image forgeries with anomalous features [C ] // 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2019 : 9535 - 9544 . DOI: 10.1109/CVPR.2019.00977 http://dx.doi.org/10.1109/CVPR.2019.00977

Bappy J H , Simons C , Nataraj L , et al . Hybrid LSTM and encoder-decoder architecture for detection of image forgeries [J ] . IEEE Transactions on Image Processing , 2019 , 28 ( 7 ): 3286 - 3300 . DOI: 10.1109/tip.2019.2895466 http://dx.doi.org/10.1109/tip.2019.2895466

Liao Xin , Chen Siliang , Chen Jiaxin , et al . CTP-net: Character texture perception network for document image forgery localization [PP/OL ] . V2. arXiv ( 2023-08-15 )[ 2025-09-23 ] . https://doi.org/10.48550/arXiv.2308.02158 https://doi.org/10.48550/arXiv.2308.02158 .

唐昊 , 李泽超 , 蒋鑫 , 等 . 基于视觉Transformer的双视图融合细粒度图像识别 [J ] . 软件学报 , 2026 , 37 ( 5 ): 2286 - 2308 .

Tang Hao , Li Zechao , Jiang Xin , et al . Dual-view fusion for fine-grained image recognition with vision transformer [J ] . Journal of Software , 2026 , 37 ( 5 ): 2286 - 2308 . (in Chinese)

Liu Ze , Hu Han , Lin Yutong , et al . Swin transformer V2: Scaling up capacity and resolution [C ] // 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2022 : 11999 - 12009 . DOI: 10.1109/cvpr52688.2022.01170 http://dx.doi.org/10.1109/cvpr52688.2022.01170

Dong Chengbo , Chen Xinru , Hu Ruohan , et al . MVSS-net: Multi-view multi-scale supervised networks for image manipulation detection [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023 , 45 ( 3 ): 3539 - 3553 . DOI: 10.1109/tpami.2022.3180556 http://dx.doi.org/10.1109/tpami.2022.3180556

Chen Zhongxi , Chen Shen , Yao Taiping , et al . Enhancing tampered text detection through frequency feature fusion and Decomposition [C ] // Computer Vision - ECCV 2024 . Cham : Springer , 2025 : 200 - 217 . DOI: 10.1007/978-3-031-73414-4_12 http://dx.doi.org/10.1007/978-3-031-73414-4_12

Yu Zeqin , Li Bin , Lin Yuzhen , et al . Learning to locate the text forgery in smartphone screenshots [C ] // ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing . Piscataway : IEEE , 2023 : 1 - 5 . DOI: 10.1109/icassp49357.2023.10095070 http://dx.doi.org/10.1109/icassp49357.2023.10095070

Du Yuning , Li Chenxia , Guo Ruoyu , et al . PP-OCR: A practical ultra lightweight OCR system [PP/OL ] . V3. arXiv ( 2020-10-15 )[ 2025-09-23 ] . https://doi.org/10.48550/arXiv.2009.09941 https://doi.org/10.48550/arXiv.2009.09941 .

Ren Ruyong , Hao Qixian , Gu Feng , et al . EMF-Net: An edge-guided multi-feature fusion network for text manipulation detection [J ] . Expert Systems with Applications , 2024 , 249 : 123548 . DOI: 10.1016/j.eswa.2024.123548 http://dx.doi.org/10.1016/j.eswa.2024.123548

Qu Chenfan , Zhong Yiwu , Guo Fengjun , et al . Revisiting tampered scene text detection in the era of generative AI [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2025 , 39 ( 1 ): 694 - 702 . DOI: 10.1609/aaai.v39i1.32051 http://dx.doi.org/10.1609/aaai.v39i1.32051

Li Zhenjiang , Sun Jingzhe , Wang Shu . Semantic-based conflict detection: Tampering detection research in bilingual scene images containing textual content [J ] . Symmetry , 2025 , 17 ( 4 ): 536 . DOI: 10.3390/sym17040536 http://dx.doi.org/10.3390/sym17040536

Tornés B M , Boros E , Doucet A , et al . Detecting forged receipts with domain-specific ontology-based entities & relations [M ] // Document analysis and recognition - ICDAR 2023 . ChamSpringer Nature Switzerland, 2023 : 184 - 199 . DOI: 10.1007/978-3-031-41682-8_12 http://dx.doi.org/10.1007/978-3-031-41682-8_12

Joren H , Gupta O , Raviv D . Learning document graphs with Attention for Image manipulation detection [C ] // Pattern Recognition and Artificial Intelligence . Cham : Springer , 2022 : 263 - 274 . DOI: 10.1007/978-3-031-09037-0_22 http://dx.doi.org/10.1007/978-3-031-09037-0_22

Guo Xiao , Song Xiufeng , Zhang Yue , et al . Rethinking vision-language model in face forensics: Multi-modal interpretable forged face detector [PP/OL ] . V1. arXiv ( 2025-03-26 )[ 2025-09-23 ] . https://doi.org/10.48550/arXiv.2503.20188 https://doi.org/10.48550/arXiv.2503.20188 .

Zhang Yue , Colman B , Guo Xiao , et al . Common sense reasoning for Deepfake detection [C ] // Computer Vision - ECCV 2024 . Cham : Springer , 2025 : 399 - 415 . DOI: 10.1007/978-3-031-73223-2_22 http://dx.doi.org/10.1007/978-3-031-73223-2_22

Shao Huiru , Qian Zhuang , Huang Kaizhu , et al . Delving into adversarial robustness on document tampering localization [C ] // Computer Vision - ECCV 2024 . Cham : Springer , 2025 : 290 - 306 . DOI: 10.1007/978-3-031-73650-6_17 http://dx.doi.org/10.1007/978-3-031-73650-6_17

Madry A , Makelov A , Schmidt L , et al . Towards deep learning models resistant to adversarial attacks [PP/OL ] . V4. arXiv ( 2019-09-04 )[ 2025-09-23 ] . https://doi.org/10.48550/arXiv.1706.06083 https://doi.org/10.48550/arXiv.1706.06083 .

Gu Jindong , Zhao Hengshuang , Tresp V , et al . SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness [C ] // Computer Vision - ECCV 2022 . Cham : Springer , 2022 : 308 - 325 . DOI: 10.1007/978-3-031-19818-2_18 http://dx.doi.org/10.1007/978-3-031-19818-2_18

Zhang Hongyang , Yu Yaodong , Jiao Jiantao , et al . Theoretically principled trade-off between robustness and accuracy [C ] // Proceedings of the International Conference on Machine Learning . 2019 , 97 : 7472 - 7482 . DOI: 10.48550/arXiv.1901.08573 http://dx.doi.org/10.48550/arXiv.1901.08573

Shao Huiru , Huang Kaizhu , Wang Wei , et al . Towards better robustness against natural corruptions in document tampering localization [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2025 , 39 ( 1 ): 703 - 710 . DOI: 10.1609/aaai.v39i1.32052 http://dx.doi.org/10.1609/aaai.v39i1.32052

Zhuo Long , Tan Shunquan , Li Bin , et al . Self-adversarial training incorporating forgery attention for image forgery localization [J ] . IEEE Transactions on Information Forensics and Security , 2022 , 17 : 819 - 834 . DOI: 10.1109/TIFS.2022.3152362 http://dx.doi.org/10.1109/TIFS.2022.3152362

Zhao Dan , Tian Xuedong . A multiscale fusion lightweight image-splicing tamper-detection model [J ] . Electronics , 2022 , 11 ( 16 ): 2621 . DOI: 10.3390/electronics11162621 http://dx.doi.org/10.3390/electronics11162621

CASIA [EB/OL ] . ( 2021-12-13 ). http://forensics.idealtest.org http://forensics.idealtest.org . DOI: 10.25009/eb.v12i30 http://dx.doi.org/10.25009/eb.v12i30

Hsu Y F , Chang S F . Detecting image splicing using geometry invariants and camera characteristics consistency [C ] // 2006 IEEE International Conference on Multimedia and Expo . Piscataway : IEEE , 2006 : 549 - 552 . DOI: 10.1109/icme.2006.262447 http://dx.doi.org/10.1109/icme.2006.262447

Jabbarlı G , Kurt M . LightFFDNets: Lightweight convolutional neural networks for rapid facial forgery detection [PP/OL ] . V1. arXiv ( 2024-11-18 )[ 2025-09-23 ] . https://doi.org/10.48550/arXiv.2411.11826 https://doi.org/10.48550/arXiv.2411.11826 .

Qu Chenfan , Liu Jian , Chen Haoxing , et al . Explainable tampered text detection via multimodal large models [PP/OL ] . V3. arXiv ( 2023-01-15 )[ 2025-09-20 ] . https://arxiv.org/abs/2412.14816 https://arxiv.org/abs/2412.14816 .

Xiao Tete , Liu Yingcheng , Zhou Bolei , et al . Unified perceptual parsing for scene understanding [M ] // Computer Vision - ECCV 2018 . ChamSpringer International Publishing , 2018 : 432 - 448 . DOI: 10.1007/978-3-030-01228-1_26 http://dx.doi.org/10.1007/978-3-030-01228-1_26

Chen L C , Zhu Yukun , Papandreou G , et al . Encoder-decoder with atrous separable convolution for semantic image segmentation [M ] // Computer Vision - ECCV 2018 . ChamSpringer International Publishing , 2018 : 833 - 851 . DOI: 10.1007/978-3-030-01234-2_49 http://dx.doi.org/10.1007/978-3-030-01234-2_49

Wang Jingdong , Sun Ke , Cheng Tianheng , et al . Deep high-resolution representation learning for visual recognition [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021 , 43 ( 10 ): 3349 - 3364 . DOI: 10.1109/TPAMI.2020.2983686 http://dx.doi.org/10.1109/TPAMI.2020.2983686

Xie Enze , Wang Wenhai , Yu Zhiding , et al . SegFormer: Simple and efficient design for semantic segmentation with transformers [J ] . Advances in Neural Information Processing Systems , 2021 , 34 : 12077 - 12090 . DOI: 10.48550/arXiv.2105.15203 http://dx.doi.org/10.48550/arXiv.2105.15203

Cheng Bowen , Schwing A G , Kirillov A . Per-pixel classification is not all you need for semantic segmentation [C ] // Proceedings of the Advances in Neural Information Processing Systems . 2021 , 34 : 17864 - 17875 . DOI: 10.48550/arXiv.2107.06278 http://dx.doi.org/10.48550/arXiv.2107.06278

Cheng Bowen , Misra I , Schwing A G , et al . Masked-attention mask transformer for universal image segmentation [C ] // 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2022 : 1280 - 1289 . DOI: 10.1109/cvpr52688.2022.00135 http://dx.doi.org/10.1109/cvpr52688.2022.00135

Liu Xiaohong , Liu Yaojie , Chen Jun , et al . PSCC-net: Progressive spatio-channel correlation network for image manipulation detection and localization [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2022 , 32 ( 11 ): 7505 - 7517 . DOI: 10.1109/TCSVT.2022.3189545 http://dx.doi.org/10.1109/TCSVT.2022.3189545

Kwon M J , Nam S H , Yu I J , et al . Learning JPEG compression artifacts for image manipulation detection and localization [J ] . International Journal of Computer Vision , 2022 , 130 ( 8 ): 1875 - 1895 . DOI: 10.1007/s11263-022-01617-5 http://dx.doi.org/10.1007/s11263-022-01617-5

李豪 , 郝文宁 , 邹世辰 , 等 . 基于Diffusion-Mamba和尺度不变损失的渐进式图像生成方法 [J ] . 电子学报 , 2025 , 53 ( 9 ): 3384 - 3396 .

Li Hao , Hao Wenning , Zou Shichen , et al . Progressive image synthesis method based on diffusion-mamba and scale-invariant loss [J ] . Acta Electronica Sinica , 2025 , 53 ( 9 ): 3384 - 3396 . (in Chinese)

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