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APFL: Active-Passive Forgery Localization for Medical Images

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Advances in Knowledge Discovery and Data Mining (PAKDD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14648))

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Abstract

Medical image forgery has become an urgent issue in academia and medicine. Unlike natural images, images in the medical field are so sensitive that even minor manipulation can produce severe consequences. Existing forgery localization methods often rely on a single image attribute and suffer from poor generalizability and low accuracy. To this end, we propose a novel active-passive forgery localization (APFL) algorithm to locate the forgery region of medical images attacked by three common forgeries: splicing, copy-move and removal. It involves two modules: a) active forgery localization, we utilize reversible watermarking to locate the fuzzy forgery region, and b) passive forgery localization, we train a lightweight model named KDU-Net through knowledge distillation to precisely locate the forgery region in the fuzzy localization result extracted by active forgery localization. The lightweight KDU-Net as a student model can achieve similar performance to RRU-Net as a teacher model, while its model capacity is only \( 24.6\%\) of RRU-Net, which facilitates fast inference for medical diagnostic devices with limited computational power. Since there are no publicly available medical tampered datasets, we manually produce tampered medical images from the real-world Ophthalmic Image Analysis (OIA) fundus image dataset. The experimental results present that APFL achieves satisfied forgery localization accuracy under the three common forgeries and shows robustness to rotation and scaling post-processing attacks.

Supported in part by the National Science Foundation of China under Grant 62272252 and Grant 62272253, the Science and Technology Development Plan of Tianjin under Grant 20JCZDJC00610 and Grant 19YFZCSF00900, the Key Research and Development Program of Guangdong under Grant 2021B0101310002, Foundation of State Key Laboratory of Public Big Data (No. PBD2022-12) and the Fundamental Research Funds for the Central Universities.

First Author and Second Author contribute equally to this work.

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Notes

  1. 1.

    https://github.com/nkicsl/OIA.

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Authors and Affiliations

  1. College of C.S., ICIC, Nankai-Huapai Joint Lab, TMCC, SysNet, Nankai University, Tianjin, China

    Nan Wang, Jiaqi Shi, Liping Yi, Gang Wang, Ming Su & Xiaoguang Liu

Authors
  1. Nan Wang
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  2. Jiaqi Shi
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  3. Liping Yi
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  4. Gang Wang
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  5. Ming Su
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  6. Xiaoguang Liu
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Corresponding author

Correspondence to Gang Wang .

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Editors and Affiliations

  1. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  4. Duke University, Durham, NC, USA

    Jian Pei

  5. National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  6. Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

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Wang, N., Shi, J., Yi, L., Wang, G., Su, M., Liu, X. (2024). APFL: Active-Passive Forgery Localization for Medical Images. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14648. Springer, Singapore. https://doi.org/10.1007/978-981-97-2238-9_14

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  • DOI: https://doi.org/10.1007/978-981-97-2238-9_14

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  • Online ISBN: 978-981-97-2238-9

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