Abstract
In recent years, there are an increasing number of incidents in which archives images have been ripped. Leak tracking is possible by adding an anti-screenshot digital watermark to an archive image. However, because an archives image's texture is single, there is a problem of low detection rate of watermark with the existing algorithm. So in order to improve the robustness of archives image anti-screenshot, we propose an anti-screenshot deep learning model (DLM): ScreenNet. It aims to enhance the background and enrich the texture with style transfer. Firstly, a preprocessing process based on style transfer is added before the archives image is input into the encoder. Secondly, the ripped images usually have moiré, so we generate a database of ripped archives images with moiré by means of a moiré network. Lastly, by improving the Stagstamp model, the watermark information is encoded/decoded through the improved ScreenNet model using the ripped archives image database as the noise layer. The experiment proves that the algorithm is able to resist anti-screenshot attacks and achieve the ability to detect watermark information to leak trace of ripped images.
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Acknowledgments
This work was partially supported by National Natural Science Foundation of China (No. 62172132) and Science and Technology Projects of National Archives Administration of China (No. 2020-X-058).
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Gu, W., Chang, CC., Bai, Y., Fan, Y., Tao, L., Li, L. (2022). Anti-screenshot Watermarking Algorithm About Archives Image Based on Deep Learning Model. In: Hsieh, SY., Hung, LJ., Klasing, R., Lee, CW., Peng, SL. (eds) New Trends in Computer Technologies and Applications. ICS 2022. Communications in Computer and Information Science, vol 1723. Springer, Singapore. https://doi.org/10.1007/978-981-19-9582-8_31
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DOI: https://doi.org/10.1007/978-981-19-9582-8_31
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