Progressive Supervision for Tampering Localization in Document Images

Huiru Shao, Kaizhu Huang, Wei Wang, Xiaowei Huang, Qiufeng Wang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Tampering localization in document images plays an important role in the field of forensic and security, which has made great progress in recent years, however it is far from being solved. In this work, we aim to improve the tampering localization performance by refining both sides of the localization model. On one hand, we propose a multi-view enhancement (MVE) module at the input side, which combines RGB image, noise residual and texture information to obtain more forensic traces for tampering localization. On the other hand, at the output side, we propose both progressive supervision (PS) and detection assistance (DA) modules to enrich more detailed supervision information. Under the progressive supervision, we calculate BCE loss at each scale to extensively explore multi-scale features, which are vital for the tampering localization. To explore the tampering detection model, we adopt a KL loss to align both tampering localization and detection scores in the DA module, benefiting the estimation of global tampered probability. In the experiments, we evaluate the proposed method on the benchmark dataset DocTamper and the results demonstrate its effectiveness.

Original languageEnglish
Title of host publicationNeural Information Processing - 30th International Conference, ICONIP 2023, Proceedings
EditorsBiao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9789819981830
Publication statusPublished - 2024
Event30th International Conference on Neural Information Processing, ICONIP 2023 - Changsha, China
Duration: 20 Nov 202323 Nov 2023

Publication series

NameCommunications in Computer and Information Science
Volume1969 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference30th International Conference on Neural Information Processing, ICONIP 2023


  • Document image
  • Multi-view enhancement
  • Progressive supervision
  • Tampering localization


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