Abstract
The article presents a method of document anonymization using generative adversarial neural networks. Unlike other anonymization methods, in the presented work, the anonymization concerns sensitive data in the form of images placed in text documents. Specifically, it is based on the CycleGAN idea and uses the U-Net model as a generator. To train the model we built a dataset with text documents with embedded real-life images, and medical images. The method is characterized by a very high efficiency, which enables the detection of 99.8% of areas where the sensitive image is located.
The work was supported by The National Centre for Research and Development (NCBR), the project no POIR.01.01.01-00-1431/19.
The project financed under the program of the Polish Minister of Science and Higher Education under the name “Regional Initiative of Excellence” in the years 2019–2023 project number 020/RID/2018/19 the amount of financing PLN 12,000,000.
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Korytkowski, M., Nowak, J., Scherer, R. (2023). Detecting Sensitive Data with GANs and Fully Convolutional Networks. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2023. Lecture Notes in Computer Science(), vol 13995. Springer, Singapore. https://doi.org/10.1007/978-981-99-5834-4_22
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