1
Candidate of Philological Sciences, Associate Professor of Department of English Philology and Intercultural Communication, Kazan Federal University, Elabuga Institute of KFU, Elabuga, Russia.
(shkilev.r.e@yandex.ru)
2
Candidate of Philological Sciences, Associate Professor of Department of English Philology and Intercultural Communication, Kazan Federal University, Elabuga Institute of KFU, Elabuga, Russia.
(kormiltseva.a.l@mail.ru)
3
Candidate of Philological Sciences, Associate Professor of Department of English Philology and Intercultural Communication, Kazan Federal University, Elabuga Institute of KFU, Elabuga, Russia.
(achaeva.m.s@mail.ru)
4
Candidate of Philological Sciences, Senior Lecturer of Department of English Philology and Intercultural Communication, Kazan Federal University, Elabuga Institute of KFU, Elabuga, Russia.
(tarasova.a.n@inbox.ru)
5
PhD in Philological Sciences, Head of the Department of Uzbek and Russian Literature, Urgench State Pedagogical Institute, Urgench, Uzbekistan.
(matquliyeva.m@mail.ru)
Abstract :
Digital document helps as the lifeblood of present communication, yet their vulnerability to tampering poses major safety anxieties. Digital text watermarking is an effective mechanism to protect the reliability of text-based data in the digital. Introducing a hidden layer of accountability and safety, allows individuals and organizations to trust the written word and make sure the truth behind all the files. Watermarking model identifies the tampering attack by inspecting the embedded signature for distortions or alterations. Watermarks can able to mechanically classify and repair themselves once tampered with, improving document resilience. Watermarking acts as a powerful tool to detect tampering attacks in digital document. By embedding strong and imperceptible watermarks in document distribution or creation, alterations are recognized by specialized procedure. This study introduces an Evolutionary Optimizer-powered Watermarking for Tampering Attack Detection in Digital Document (EO-WTAD3) model. The main intention of EO-WTAD3 approach is to support textual integrity using the applications of metaheuristic optimizer algorithm based watermarking technique for detecting tampering attacks in digital document. In the EO-WTAD3 method, a digital watermarking method has been proposed for the ownership verification and document copyright protection using data mining concept. Moreover, the EO-WTAD3 technique utilizes the concepts of data mining to define appropriate characteristics from the document for embedding watermarks. Moreover, fractional gorilla troops optimization (FGTO) algorithm can be applied for the assortment of optimal situation of watermarks in the content, ensuring both imperceptibility and strong to tamper. The performance validation of the EO-WTAD3 methodology takes place employing multiple datasets. The extensive result analysis portrayed that the EO-WTAD3 system accomplishes improve solution with other existing approaches with respect distinct aspects.
Keywords :
Digital Watermarking; Tampering Attack Detection; Digital Documents; Metaheuristic; Fractional Gorilla Troops Optimization
References :
[1] Smarandache, F., Neutrosophic set a generalization of the intuitionistic fuzzy sets. Inter. J. Pure Appl. Math., 24, 287 – 297, 2005.
[2] B. Singh and M. K. Sharma, ‘‘Tamper detection technique for document images using zero watermarking in wavelet domain,’’ Comput. Electr. Eng., vol. 89, Jan. 2021, Art. no. 106925.
[3] F. N. Al-Wesabi, S. Alzahrani, F. Alyarimi, M. Abdul, N. Nemri, and M. M. Almazah, ‘‘A reliable NLP scheme for English text watermarking based on contents interrelationship,’’ Comput. Syst. Sci. Eng., vol. 37, no. 3, pp. 297–311, 2021.
[4] M. L. P. Gort, M. Olliaro, A. Cortesi, and C. F. Uribe, ‘‘Semantic-driven watermarking of relational textual databases,’’ Expert Syst. Appl., vol. 167, Apr. 2021, Art. no. 114013.
[5] U. Khadam, M. M. Iqbal, M. Alruily, M. A. Al Ghamdi, M. Ramzan, and S. H. Almotiri, ‘‘Text data security and privacy in the Internet of Things: Threats, challenges, and future directions,’’ Wireless Commun. Mobile Comput., vol. 2020, pp. 1–15, Feb. 2020.
[6] X. Wang and Y. Jin, ‘‘A high-capacity text watermarking method based on geometric micro-distortion,’’ in Proc. 26th Int. Conf. Pattern Recognit. (ICPR), Aug. 2022, pp. 1749–1755.
[7] S. Abdelnabi and M. Fritz, ‘‘Adversarial watermarking transformer: Towards tracing text provenance with data hiding,’’ in Proc. IEEE Symp. Secur. Privacy (SP), May 2021, pp. 121–140.
[8] N. Mir and M. A. U. Khan, ‘‘Copyright protection for online text information: Using watermarking and cryptography,’’ in Proc. 3rd Int. Conf. Comput. Appl. Inf. Secur. (ICCAIS), Mar. 2020, pp. 1–4.
[9] M. Alamgeer, F. N. Al-Wesabi, H. G. Iskandar, I. Khan, N. Nemri, M. Medani, M. A. Al-Hagery, and A. M. Al-Sharafi, ‘‘Smart-fragile authentication scheme for robust detecting of tampering attacks on English text,’’ Comput., Mater. Continua, vol. 71, no. 2, pp. 2497–2513, 2022.
[10] Y. Chou, K. Anggriani, N. Wu, and M. Hwang, ‘‘Research on E-book text copyright protection and anti-tampering technology,’’ Int. J. Netw. Secur., vol. 23, no. 5, pp. 739–749, 2021.
[11] Qu, C., Liu, C., Liu, Y., Chen, X., Peng, D., Guo, F. and Jin, L., 2023. Towards Robust Tampered Text Detection in Document Image: New Dataset and New Solution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5937-5946).
[12] Al-Wesabi, F.N., Alrowais, F., Mohamed, H.G., Al Duhayyim, M., Hilal, A.M. and Motwakel, A., 2023. Heuristic Optimization Algorithm Based Watermarking on Content Authentication and Tampering Detection for English Text. IEEE Access.
[13] Sinha Roy, S., Basu, A., Chattopadhyay, A. and Kamal, R., 2021. Hardware execution of a saliency map based digital image watermarking framework. Multimedia Tools and Applications, 80(18), pp.27245-27258.
[14] Mustafa Hilal, A., Al-Wesabi, F.N., Alamgeer, M., Hamza, M., Mahzari, M. and Almekhlafi, M.A., 2022. An Optimal Text Watermarking Method for Sensitive Detecting of Illegal Tampering Attacks. Computers, Materials & Continua, 70(3).
[15] Wang, S., Yuan, W., Zhang, Z. and Wang, L., 2023. Speech watermarking based tamper detection and recovery scheme with high tolerable tamper rate. Multimedia Tools and Applications, pp.1-19.
[16] Mubeen, Z., Afzal, M., Ali, Z., Khan, S. and Imran, M., 2021. Detection of impostor and tampered segments in audio by using an intelligent system. Computers & Electrical Engineering, 91, p.107122.
[17] Alghamdi, A.S., Naz, S., Saeed, A., Al Solami, E., Kamran, M. and Alkatheiri, M.S., 2022. A novel database watermarking technique using blockchain as trusted third party. Computers, Material and Continua (CMC), 70(1), pp.1585-1601.
[18] Semyonov, N., Puzis, R., Shabtai, A. and Katz, G., 2023. ReMark: Receptive Field based Spatial WaterMark Embedding Optimization using Deep Network. arXiv preprint arXiv:2305.06786.
[19] Banerjee, A., Shivakumara, P., Acharya, P., Pal, U. and Canet, J.L., 2022, August. TWD: A New Deep E2E Model for Text Watermark/Caption and Scene Text Detection in Video. In 2022 26th International Conference on Pattern Recognition (ICPR) (pp. 1492-1498). IEEE.
[20] Khadam, U., Iqbal, M.M., Azam, M.A., Khalid, S., Rho, S. and Chilamkurti, N., 2019. Digital watermarking technique for text document protection using data mining analysis. IEEE Access, 7, pp.64955-64965.
[21] Mali, S.D. and Agilandeeswari, L., 2023. Non-redundant shift-invariant complex wavelet transform and fractional gorilla troops optimization-based deep convolutional neural network for video watermarking. Journal of King Saud University-Computer and Information Sciences, 35(8), p.101688.
[22] F. N. Al-Wesabi, ‘‘Text analysis-based watermarking approach for tampering detection of English text,’’ Comput., Mater. Continua, vol. 67, no. 3, pp. 3701–3719, 2021.
| Style | # |
|---|---|
| MLA | Roman Shkilev, Alevtina Kormiltseva, Marina Achaeva, Aiziryak Tarasova, Marguba Matquliyeva. "Fortifying Textual Integrity: Evolutionary Optimization-powered Watermarking for Tampering Attack Detection in Digital Documents." Fusion: Practice and Applications, Vol. 14, No. 2, 2024 ,PP. 97-108 (Doi : https://doi.org/10.54216/FPA.140208) |
| APA | Roman Shkilev, Alevtina Kormiltseva, Marina Achaeva, Aiziryak Tarasova, Marguba Matquliyeva. (2024). Fortifying Textual Integrity: Evolutionary Optimization-powered Watermarking for Tampering Attack Detection in Digital Documents. Journal of Fusion: Practice and Applications, 14 ( 2 ), 97-108 (Doi : https://doi.org/10.54216/FPA.140208) |
| Chicago | Roman Shkilev, Alevtina Kormiltseva, Marina Achaeva, Aiziryak Tarasova, Marguba Matquliyeva. "Fortifying Textual Integrity: Evolutionary Optimization-powered Watermarking for Tampering Attack Detection in Digital Documents." Journal of Fusion: Practice and Applications, 14 no. 2 (2024): 97-108 (Doi : https://doi.org/10.54216/FPA.140208) |
| Harvard | Roman Shkilev, Alevtina Kormiltseva, Marina Achaeva, Aiziryak Tarasova, Marguba Matquliyeva. (2024). Fortifying Textual Integrity: Evolutionary Optimization-powered Watermarking for Tampering Attack Detection in Digital Documents. Journal of Fusion: Practice and Applications, 14 ( 2 ), 97-108 (Doi : https://doi.org/10.54216/FPA.140208) |
| Vancouver | Roman Shkilev, Alevtina Kormiltseva, Marina Achaeva, Aiziryak Tarasova, Marguba Matquliyeva. Fortifying Textual Integrity: Evolutionary Optimization-powered Watermarking for Tampering Attack Detection in Digital Documents. Journal of Fusion: Practice and Applications, (2024); 14 ( 2 ): 97-108 (Doi : https://doi.org/10.54216/FPA.140208) |
| IEEE | Roman Shkilev, Alevtina Kormiltseva, Marina Achaeva, Aiziryak Tarasova, Marguba Matquliyeva, Fortifying Textual Integrity: Evolutionary Optimization-powered Watermarking for Tampering Attack Detection in Digital Documents, Journal of Fusion: Practice and Applications, Vol. 14 , No. 2 , (2024) : 97-108 (Doi : https://doi.org/10.54216/FPA.140208) |