Abstract
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.
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Acknowledgements
This research was funded by National Natural Science Foundation of China (NSFC) no.62276258, Jiangsu Science and Technology Programme no. BE2020006-4, European Union’s Horizon 2020 research and innovation programme no. 956123, and UK EPSRC under projects [EP/T026995/1]
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Shao, H., Huang, K., Wang, W., Huang, X., Wang, Q. (2024). Progressive Supervision for Tampering Localization in Document Images. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1969. Springer, Singapore. https://doi.org/10.1007/978-981-99-8184-7_11
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DOI: https://doi.org/10.1007/978-981-99-8184-7_11
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