Abstract
Under the new international situation, every country attaches great importance to the information security of the Internet, among which webpage anti-tampering is one of the top priorities. The premise of webpage tampering detection is to obtain webpages with different timestamps. Because of the diversity of website structure, it is necessary to make reasonable crawling strategy when using the traditional crawler method to obtain web pages, which leads to the problem of inflexible application. To address this, this paper adopts deep learning approach for detecting webpage text and thus acquiring web page information. The improved Faster-RCNN model is used to detect webpages and the resnet network is used to extract text features. In view of the feature of long text image font, the square convolution kernel of the traditional network is replaced by a rectangular convolution kernel to better fit the long and narrow features of the text; for the characteristics of dense text lines, the traditional NMS algorithm is replaced by the Soft-NMS algorithm to reduce the missed detection of dense regions. The experiments show that this algorithm has a better detection effect, which is important for network information security.
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Acknowledgements
We acknowledge funding from the sub project of national key R & D plan covid-19 patient rehabilitation training posture monitoring bracelet based on 4G network (Grant No.2021YFC0863200–6), the Hebei College and Middle School Students Science and Technology Innovation Ability Cultivation Special Project (Grant No.22E50075D), (Grant No.2021H011404) and (Grant No.2021H010203).
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Gao, J., Zhao, M., Wang, J., Zhao, Y., Ma, L., Yu, P. (2023). Webpage Text Detection Based on Improved Faster-RCNN Model. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_5
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