Abstract
Nowadays, with the rapid development of network technology and the rapid increase of information, the importance of data is becoming more and more obvious, and the requirements of data security are also improving. Using the detection and tracking technology based on data watermarking can quickly and effectively obtain the information of data watermarking, and trace the source of possible attacks, so as to improve the security of data. The traditional passive data watermarking detection and tracking technology has some limitations in the real-time tracking of possible attacks. Therefore, this paper proposed an active data watermark detection system. The related watermark embedding and detection technologies are firstly studied. The system architecture is proposed where primary modules and submodules are described. The proposed system is designed to be integrated into the network nodes and servers in order to embed and detect the watermark information in real time. In addition, the watermarking parameters can be adjusted according to the sensed network environment. The proposed system can be implemented in the industrial departments that own a large amount of sensitive data so that the data transmission can be monitored and traced.
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References
Ding, W., Ming, Y., Cao, Z., Lin, C.-T.: A generalized deep neural network approach for digital watermarking analysis. IEEE Trans. Emerg. Top. Comput. Intell. 6(3), 613–627 (2022)
Shankar, A., Kannammal, A.: A hybrid of watermark scheme with encryption to improve security of medical images. In: 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pp. 226–233 (2021)
Chen, W., Zeng, B., Zheng, P.: A robust watermarking scheme for encrypted JPEG bitstreams with format-compliant encryption. In: 2019 18th IEEE International Conference on Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), pp. 397–403 (2019)
Shen, X., Zhang, Y., Wang, T., Sun, Y.: Relational database watermarking for data tracing. In: 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 224–231 (2020)
Gavilanes, G.B.: Persons counter through Wi-Fi’s passive sniffing for IoT. In: 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM), pp. 1–6 (2018)
Kim, W., Lee, K.: Digital watermarking for protecting audio classification datasets. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2842–2846 (2020)
Kim, J., Ko, W.-H., Kumar, P.R.: Cyber-security through dynamic watermarking for 2-rotor aerial vehicle flight control systems. In: 2021 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1277–1283 (2021)
Ma, Z., Zhang, W., Fang, H., Dong, X., Geng, L., Yu, N.: Local geometric distortions resilient watermarking scheme based on symmetry. IEEE Trans. Circuits Syst. Video Technol. 31(12), 4826–4839 (2021)
Haddad, S., Coatrieux, G., Moreau-Gaudry, A., Cozic, M.: Joint watermarking-encryption-JPEG-LS for medical image reliability control in encrypted and compressed domains. IEEE Trans. Inf. Forensics Secur. 15, 2556–2569 (2020)
Houmansadr, A., Kiyavash, N., Borisov, N.: Non-blind watermarking of network flows. IEEE/ACM Trans. Netw. 22(4), 1232–1244 (2014)
Azizian, B., Ghaemmaghami, S.: Tampering detection and restoration of compressed video. In: 2018 15th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC), pp. 1–5 (2018)
Rakhmawati, L., Wirawan, Suwadi: Image fragile watermarking with two authentication components for tamper detection and recovery. In: 2018 International Conference on Intelligent Autonomous Systems (ICoIAS), pp. 35–38 (2018)
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Zhang, L., Zhang, X., Yang, T. (2023). Design of an Active Data Watermark Detection System. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_43
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DOI: https://doi.org/10.1007/978-981-99-3300-6_43
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