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A Fragile Watermarking Approach for Earth Observation Data Integrity Protection

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Space Data Management

Part of the book series: Studies in Big Data ((SBD,volume 141))

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Abstract

Volumes of data generated from remote sensing activities have grown larger in the last years due to the increasing number of satellites orbiting the earth and the development of drones and their sensors. Multiple online providers allow obtaining the data of a geographical location from a given time period. Furthermore, free downloading of data sets that store information in different formats is possible. The easy access, portability, and distribution make it possible to perform data tampering and spread inaccurate information, which leads to wrong data-based conclusions. This work proposes a fragile watermarking approach to detect Earth observation data contradictions and avoid economic losses that could compromise organizations’ performance. We design an architecture to link attributes and generate a signal that is stored in other fragments of the same data, allowing double-checking of data quality before proceeding with decision-making. Our method is proven to be very effective in spotting unauthorized updates.

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Notes

  1. 1.

    Campbell and Wynne [7] define remote sensing as the practice of deriving information about Earth’s land and water surfaces using images acquired from an overhead perspective, using electromagnetic radiation in one or more regions of the electromagnetic spectrum reflected or emitted from Earth’s surface.

  2. 2.

    A GIS is a computer-based system that allows managing georeferenced data, including functionalities of capture, preparation, storage maintenance, and presentation [18].

  3. 3.

    The incremental watermarking requirement defines that, for a watermarked database, every time a new data is inserted or updated, a mark should be computed and embedded, if the watermarking technique and the parameters used for the synchronization defined so.

  4. 4.

    More details about the structure of a database relation according to the relational model can be found in [11, 24].

  5. 5.

    Details about the implementation of functions \(\Delta (\cdot )\), \(\Xi (\cdot )\), \(\Lambda (\cdot )\), and \(\Gamma (\cdot )\) can be found in [13, 14, 22].

  6. 6.

    Detailed information regarding Landsat 9 imagery can be found in [20].

References

  1. Agrawal, R., Haas, P.J., Kiernan, J.: Watermarking relational data: framework, algorithms and analysis. VLDB J. 12, 157–169 (2003)

    Article  Google Scholar 

  2. Agrawal, R., Kiernan, J.: Watermarking relational databases. In: VLDB’02: Proceedings of the 28th International Conference on Very Large Databases, pp. 155–166. Elsevier (2002)

    Google Scholar 

  3. Alqassab, A., Alanezi, M.: Reversible watermarking approach for ensuring the integrity of private databases. In: Next Generation of Internet of Things: Proceedings of ICNGIoT 2022, pp. 563–572. Springer (2022)

    Google Scholar 

  4. Amrit, P., Singh, A.K.: Survey on watermarking methods in the artificial intelligence domain and beyond. Comput. Commun. 188, 52–65 (2022)

    Article  Google Scholar 

  5. Barni, M., Bartolini, F.: Watermarking Systems Engineering: Enabling Digital Assets Security and Other Applications. CRC Press (2004)

    Google Scholar 

  6. Camara, L., Li, J., Li, R., Xie, W.: Distortion-free watermarking approach for relational database integrity checking. Math. Probl. Eng. (2014)

    Google Scholar 

  7. Campbell, J.B., Wynne, R.H.: Introduction to Remote Rensing, 5th edn. Guilford Press, New York, US (2011)

    Google Scholar 

  8. Chang, C.C., Nguyen, T.S., Lin, C.C.: A blind reversible robust watermarking scheme for relational databases. Sci. World J. (2013)

    Google Scholar 

  9. Commission, E.: Earth observation (2023). https://joint-research-centre.ec.europa.eu/scientific-activities-z/earth-observation_en

  10. Cox, I., Miller, M., Bloom, J., Fridrich, J., Kalker, T.: Digital Watermarking and Steganography. Morgan Kaufmann (2007)

    Google Scholar 

  11. Date, C.: An Introduction to Database Systems, 8th edn. Addison-Wesley Longman Publishing Co., Inc, USA (2003)

    Google Scholar 

  12. Farfoura, M.E., Horng, S.J., Lai, J.L., Run, R.S., Chen, R.J., Khan, M.K.: A blind reversible method for watermarking relational databases based on a time-stamping protocol. Expert Syst. Appl. 39(3), 3185–3196 (2012)

    Article  Google Scholar 

  13. Gort, M.L.P., Díaz, E.A., Uribe, C.F.: A highly-reliable virtual primary key scheme for relational database watermarking techniques. In: 2017 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 55–60. IEEE (2017)

    Google Scholar 

  14. Gort, M.L.P., Feregrino-Uribe, C., Cortesi, A., Fernández-Peña, F.: HQR-scheme: a high quality and resilient virtual primary key generation approach for watermarking relational data. Expert Syst. Appl. 138, 112770 (2019)

    Google Scholar 

  15. Gort, M.L.P., Olliaro, M., Cortesi, A.: Relational data watermarking resilience to brute force attacks in untrusted environments. Expert Syst. Appl. 212, 118713 (2023)

    Google Scholar 

  16. Hamadou, A., Camara, L., Issaka Hassane, A.A., Naroua, H.: Reversible fragile watermarking scheme for relational database based on prediction-error expansion. Math. Probl. Eng. 2020, 1–9 (2020)

    Article  Google Scholar 

  17. Harris, R., Baumann, I.: Open data policies and satellite earth observation. Space Policy 32, 44–53 (2015)

    Article  Google Scholar 

  18. Huisman, O., de By, R.A., et al.: Principles of geographic information systems. ITC Educational Textbook Series, vol. 1, p. 17 (2009)

    Google Scholar 

  19. Khanduja, V., Verma, O.P., Chakraverty, S.: Watermarking relational databases using bacterial foraging algorithm. Multimed. Tools Appl. 74, 813–839 (2015)

    Article  Google Scholar 

  20. Masek, J.G., Wulder, M.A., Markham, B., McCorkel, J., Crawford, C.J., Storey, J., Jenstrom, D.T.: Landsat 9: empowering open science and applications through continuity. Remote Sens. Environ. 248, 111968 (2020)

    Google Scholar 

  21. Olliaro, M., Gort, M.L.P., Cortesi, A.: Empirical analysis of the impact of queries on watermarked relational databases. Expert Syst. Appl. 204, 117491 (2022)

    Article  Google Scholar 

  22. Pérez Gort, M.L., Feregrino Uribe, C., Nummenmaa, J.: A minimum distortion: High capacity watermarking technique for relational data. In: Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, pp. 111–121 (2017)

    Google Scholar 

  23. Pérez Gort, M.L., Feregrino-Uribe, C., Cortesi, A., Fernández-Peña, F.: A double fragmentation approach for improving virtual primary key-based watermark synchronization. IEEE Access 8, 61504–61516 (2020)

    Article  Google Scholar 

  24. Pérez Gort, M.L., Olliaro, M., Cortesi, A.: Reducing multiple occurrences of meta-mark selection in relational data watermarking. IEEE Access 10, 62210–62231 (2022)

    Article  Google Scholar 

  25. Pettorelli, N., Ryan, S., Mueller, T., Bunnefeld, N., Jedrzejewska, B., Lima, M., Kausrud, K.: The normalized difference vegetation index (NDVI): unforeseen successes in animal ecology. Climate Res. 46(1), 15–27 (2011)

    Article  Google Scholar 

  26. Ritter, N., Ruth, M., Grissom, B.B., Galang, G., Haller, J., Stephenson, G., Covington, S., Nagy, T., Moyers, J., Stickley, J., et al.: GeoTIFF format specification GeoTIFF revision 1.0. SPOT Image Corp, p. 1 (2000)

    Google Scholar 

  27. Sara, U., Akter, M., Uddin, M.S.: Image quality assessment through FSIM, SSIM, MSE and PSN-a comparative study. J. Comput. Commun. 7(3), 8–18 (2019)

    Article  Google Scholar 

  28. Siledar, S., Tamane, S.: A distortion-free watermarking approach for verifying integrity of relational databases. In: 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC), pp. 192–195. IEEE (2020)

    Google Scholar 

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Acknowledgements

This work was partially supported by SERICS (PE00000014) under the NRRP MUR program funded by the EU—NGEU.

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Authors and Affiliations

  1. Ca’ Foscari University of Venice, Scientific Campus, Via Torino 155, 30172, Mestre, Venice, Italy

    Maikel Lázaro Pérez Gort & Agostino Cortesi

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  1. Maikel Lázaro Pérez Gort
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  2. Agostino Cortesi
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Correspondence to Maikel Lázaro Pérez Gort .

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  1. Università Ca’ Foscari Venezia, Venice, Italy

    Agostino Cortesi

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Pérez Gort, M., Cortesi, A. (2024). A Fragile Watermarking Approach for Earth Observation Data Integrity Protection. In: Cortesi, A. (eds) Space Data Management. Studies in Big Data, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-97-0041-7_3

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