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ICDAR 2023 Competition on Detecting Tampered Text in Images

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Document Analysis and Recognition - ICDAR 2023 (ICDAR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14188))

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Abstract

Document analysis and recognition techniques are evolving quickly and have been widely used in real-world applications. However, detecting tampered text in images is rarely studied. Existing image forensics research mainly focuses on detecting tampered objects in natural images. Text manipulation in images exhibits different characteristics, e.g.,, text consistency, imperceptibility, etc., which bring new challenges for image forensics. Therefore, We organized the ICDAR 2023 Competition on Detecting Tampered Text in Images (DTTI) and established a new dataset named TII, which consists of 11,385 images. 5,500 images are tampered using various manipulation techniques and annotated by pixel-level masks. Two tasks are set up: text manipulation classification and text manipulation detection. The contest started on 15th February, 2023 and ended on 20th March, 2023. Over a thousand teams were registered for the competition, with 277 and 176 valid submissions in each task. In this competition report, we describe the details of the proposed dataset, tasks, evaluation protocols and the results summaries. We hope that this competition could promote the research of text manipulation detection in images.

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Notes

  1. 1.

    https://tianchi.aliyun.com/competition/entrance/532048/introduction.

  2. 2.

    https://tianchi.aliyun.com/competition/entrance/532052/introduction.

  3. 3.

    https://tianchi.aliyun.com/competition/entrance/532048/rankingList.

  4. 4.

    https://tianchi.aliyun.com/competition/entrance/532052/rankingList.

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Acknowledgments

This competition is supported by the National Natural Science Foundation (NSFC#62225603).

Author information

Authors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Dongliang Luo, Yu Zhou, Yuliang Liu, Enming Zhang, Biao Yang, Ziming Huang & Xiang Bai

  2. Alibaba Group, Hangzhou, China

    Rui Yang, Xianjin Liu & Jishen Zeng

  3. South China University of Technology, Guangzhou, China

    Lianwen Jin

Authors
  1. Dongliang Luo
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  2. Yu Zhou
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  3. Rui Yang
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  4. Yuliang Liu
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  5. Xianjin Liu
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  6. Jishen Zeng
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  7. Enming Zhang
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  8. Biao Yang
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  9. Ziming Huang
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  10. Lianwen Jin
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  11. Xiang Bai
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Corresponding author

Correspondence to Xiang Bai .

Editor information

Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. Adobe, College Park, MN, USA

    Rajiv Jain

  3. Osaka Metropolitan University, Osaka, Japan

    Koichi Kise

  4. Rochester Institute of Technology, Rochester, NY, USA

    Richard Zanibbi

Appendices

Appendix

A More Examples from TTI

Figure 3 gives more examples from TII dataset, including tampered documents and their ground truth.

Fig. 3.
figure 3

More examples from TTI dataset. The forged images and their ground truth are presented in the odd rows and even rows, respectively.

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Luo, D. et al. (2023). ICDAR 2023 Competition on Detecting Tampered Text in Images. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14188. Springer, Cham. https://doi.org/10.1007/978-3-031-41679-8_36

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  • DOI: https://doi.org/10.1007/978-3-031-41679-8_36

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