A Comprehensive Study on Digital Watermarking for Security Threats and Research Directions

Authors

  • Sambhaji Marutirao Shedole School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, India https://orcid.org/0009-0000-9821-926X
  • V Santhi School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, India

DOI:

https://doi.org/10.59461/ijdiic.v4i1.160

Keywords:

Digital Image Watermarking , Steganography, Artificial Intelligence, Information hiding, Security

Abstract

With the growing popularity of the Internet of Things (IoT) and the proliferation of AI-powered smart devices, a vast amount of digital data is being stored and shared on public platforms such as LinkedIn, Facebook, Twitter, Flickr, and other social media websites. Among these, digital images are the most commonly used medium for data sharing. Recent reports indicate that Google Photos stores over a billion images per week. Additionally, digital images play a crucial role in various applications, including social media, smart healthcare, intelligent transportation systems, industrial automation, robotics, the film industry, legal systems, news and insurance industries, and business sectors. However, the misuse of these images raises significant concerns regarding privacy and security. Identity theft, a growing issue in the 21st century, is a primary contributor to fraud in India and other countries. Thus, ensuring the security of digital images is a critical challenge that needs to be addressed. Currently, watermarking algorithms offer the most cost-effective solution for securing digital images. Watermarking involves embedding hidden marks within digital content to enhance its security. The key benefits of watermarking include: 1. Reducing bandwidth and storage demands, 2. Preventing copyright infringement and ownership disputes, 3. Protecting against tampering, and 4. Serving as an authentication keyword. Watermarking has gained significant traction in various fields, including cybersecurity and e-governance; given the growing importance of digital watermarking from a security perspective, this study aims to provide a comprehensive analysis of watermarking techniques and their evaluation methods across different applications. The findings of this extensive study are presented in this paper. Social media and healthcare. The primary objective of watermarking research is to enhance robustness, watermark capacity, and invisibility—an intricate trade-off among these factors. However, many existing watermarking techniques lack adequate security measures. Various approaches are available for both implementing and evaluating digital watermarking algorithms.

Downloads

Download data is not yet available.

References

P. Aberna and L. Agilandeeswari, “Digital image and video watermarking: methodologies, attacks, applications, and future directions,” Multimed Tools Appl, vol. 83, no. 2, pp. 5531–5591, Jan. 2024, doi: 10.1007/s11042-023-15806-y.

D. K. Mahto and A. K. Singh, “A survey of color image watermarking: State-of-the-art and research directions,” Computers & Electrical Engineering, vol. 93, p. 107255, Jul. 2021, doi: 10.1016/j.compeleceng.2021.107255.

A. Anand and A. K. Singh, “Watermarking techniques for medical data authentication: a survey,” Multimed Tools Appl, vol. 80, no. 20, pp. 30165–30197, Aug. 2021, doi: 10.1007/s11042-020-08801-0.

S. Gull and S. A. Parah, “Advances in medical image watermarking: a state of the art review,” Multimed Tools Appl, vol. 83, no. 1, pp. 1407–1447, Jan. 2024, doi: 10.1007/s11042-023-15396-9.

S. Kashifa, S. Tangeda, U. K. Sree, and V. M. Manikandan, “Digital Image Watermarking and Its Applications: A Detailed Review,” in 2023 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), IEEE, Feb. 2023, pp. 1–7. doi: 10.1109/SCEECS57921.2023.10063033.

H. K. Singh and A. K. Singh, “Comprehensive review of watermarking techniques in deep-learning environments,” J Electron Imaging, vol. 32, no. 03, Nov. 2022, doi: 10.1117/1.JEI.32.3.031804.

S. P. Mohanty, A. Sengupta, P. Guturu, and E. Kougianos, “Everything You Want to Know About Watermarking: From Paper Marks to Hardware Protection,” IEEE Consumer Electronics Magazine, vol. 6, no. 3, pp. 83–91, Jul. 2017, doi: 10.1109/MCE.2017.2684980.

P. Amrit and A. K. Singh, “Survey on watermarking methods in the artificial intelligence domain and beyond,” Comput Commun, vol. 188, pp. 52–65, Apr. 2022, doi: 10.1016/j.comcom.2022.02.023.

O. P. Singh, A. K. Singh, G. Srivastava, and N. Kumar, “Image watermarking using soft computing techniques: A comprehensive survey,” Multimed Tools Appl, vol. 80, no. 20, pp. 30367–30398, Aug. 2021, doi: 10.1007/s11042-020-09606-x.

R. Sinhal, I. A. Ansari, and C. W. Ahn, “Blind Image Watermarking for Localization and Restoration of Color Images,” IEEE Access, vol. 8, pp. 200157–200169, 2020, doi: 10.1109/ACCESS.2020.3035428.

T. Huynh-The et al., “Selective bit embedding scheme for robust blind color image watermarking,” Inf Sci (N Y), vol. 426, pp. 1–18, Feb. 2018, doi: 10.1016/j.ins.2017.10.016.

U. A. Bhatti et al., “Hybrid Watermarking Algorithm Using Clifford Algebra With Arnold Scrambling and Chaotic Encryption,” IEEE Access, vol. 8, pp. 76386–76398, 2020, doi: 10.1109/ACCESS.2020.2988298.

K. R. Perez-Daniel, F. Garcia-Ugalde, and V. Sanchez, “Watermarking of HDR Images in the Spatial Domain With HVS-Imperceptibility,” IEEE Access, vol. 8, pp. 156801–156817, 2020, doi: 10.1109/ACCESS.2020.3019517.

W.-W. Hu, R.-G. Zhou, A. El-Rafei, and S.-X. Jiang, “Quantum Image Watermarking Algorithm Based on Haar Wavelet Transform,” IEEE Access, vol. 7, pp. 121303–121320, 2019, doi: 10.1109/ACCESS.2019.2937390.

E. Fragoso-Navarro, M. Cedillo-Hernandez, M. Nakano-Miyatake, A. Cedillo-Hernandez, and H. M. Perez-Meana, “Visible Watermarking Assessment Metrics Based on Just Noticeable Distortion,” IEEE Access, vol. 6, pp. 75767–75788, 2018, doi: 10.1109/ACCESS.2018.2883322.

N. M. Makbol, B. E. Khoo, and T. H. Rassem, “Block‐based discrete wavelet transform‐singular value decomposition image watermarking scheme using human visual system characteristics,” IET Image Process, vol. 10, no. 1, pp. 34–52, Jan. 2016, doi: 10.1049/iet-ipr.2014.0965.

W. Puech and J. M. Rodrigues, “A New Crypto-Watermarking Method for Medical Images Safe Transfer,” Sep. 2004. https://www.researchgate.net/publication/253934763

A. K. Singh, “Robust and distortion control dual watermarking in LWT domain using DCT and error correction code for color medical image,” Multimed Tools Appl, vol. 78, no. 21, pp. 30523–30533, Nov. 2019, doi: 10.1007/s11042-018-7115-x.

D. K. Mahto, A. K. Singh, K. N. Singh, O. P. Singh, and A. K. Agrawal, “Robust Copyright Protection Technique with High-embedding Capacity for Color Images,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 11, pp. 1–12, Nov. 2024, doi: 10.1145/3580502.

D. K. Mahto, A. Anand, and A. K. Singh, "Hybrid optimization-based robust watermarking using denoising convolutional neural network," Soft comput, vol. 26, no. 16, pp. 8105–8116, Aug. 2022, doi: 10.1007/s00500-022-07155-z.

A. Fatahbeygi and F. Akhlaghian Tab, “A highly robust and secure image watermarking based on classification and visual cryptography,” Journal of Information Security and Applications, vol. 45, pp. 71–78, Apr. 2019, doi: 10.1016/j.jisa.2019.01.005.

M. Islam, A. Roy, and R. H. Laskar, “SVM-based robust image watermarking technique in LWT domain using different sub-bands,” Neural Comput Appl, vol. 32, no. 5, pp. 1379–1403, Mar. 2020, doi: 10.1007/s00521-018-3647-2.

H.-Y. Yang, X.-Y. Wang, Y. Zhang, and M. E-nuo, “Robust digital watermarking in PDTDFB domain based on least squares support vector machine,” Eng Appl Artif Intell, vol. 26, no. 9, pp. 2058–2072, Oct. 2013, doi: 10.1016/j.engappai.2013.04.014.

V. S. Verma, R. K. Jha, and A. Ojha, “Digital watermark extraction using support vector machine with principal component analysis based feature reduction,” J Vis Commun Image Represent, vol. 31, pp. 75–85, Aug. 2015, doi: 10.1016/j.jvcir.2015.06.001.

S. Vairaprakash and A. Shenbagavalli, “A Discrete Rajan Transform-based robustness improvement encrypted watermark scheme backed by Support Vector Machine,” Computers & Electrical Engineering, vol. 70, pp. 826–843, Aug. 2018, doi: 10.1016/j.compeleceng.2017.12.029.

X.-Y. Wang, E.-N. Miao, and H.-Y. Yang, “A new SVM-based image watermarking using Gaussian–Hermite moments,” Appl Soft Comput, vol. 12, no. 2, pp. 887–903, Feb. 2012, doi: 10.1016/j.asoc.2011.10.003.

H. Peng, J. Wang, and W. Wang, “Image watermarking method in multiwavelet domain based on support vector machines,” Journal of Systems and Software, vol. 83, no. 8, pp. 1470–1477, Aug. 2010, doi: 10.1016/j.jss.2010.03.006.

S. Ramly, S. A. Aljunid, and H. Shaker Hussain, “SVM-SS Watermarking Model for Medical Images,” in Digital Enterprise and Information Systems, Ezendu Ariwa and Eyas El-Qawasmeh, Eds., Springer Nature, 2011, pp. 372–386. doi: 10.1007/978-3-642-22603-8_34.

R. Choudhary and G. Parmar, “A robust image watermarking technique using 2-level discrete wavelet transform (DWT),” in 2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS), IEEE, Nov. 2016, pp. 120–124. doi: 10.1109/CCIntelS.2016.7878213.

S. S. Gonge and A. Ghatol, “Composition of DCT-SVD Image Watermarking and Advanced Encryption Standard Technique for Still Image,” in Intelligent Systems Technologies and Applications, 2016, pp. 85–97. doi: 10.1007/978-3-319-47952-1_7.

L. Laouamer, M. AlShaikh, L. Nana, and A. C. Pascu, “Robust watermarking scheme and tamper detection based on threshold versus intensity,” Journal of Innovation in Digital Ecosystems, vol. 2, no. 1–2, pp. 1–12, Dec. 2015, doi: 10.1016/j.jides.2015.10.001.

A. Karmakar, A. Phadikar, B. S. Phadikar, and G. Kr. Maity, “A blind video watermarking scheme resistant to rotation and collusion attacks,” Journal of King Saud University - Computer and Information Sciences, vol. 28, no. 2, pp. 199–210, Apr. 2016, doi: 10.1016/j.jksuci.2014.06.019.

R. Sinhal and I. A. Ansari, “Machine learning based multipurpose medical image watermarking,” Neural Comput Appl, vol. 35, no. 31, pp. 23041–23062, Nov. 2023, doi: 10.1007/s00521-023-08457-5.

R. Sinhal, D. K. Jain, and I. A. Ansari, “Machine learning based blind color image watermarking scheme for copyright protection,” Pattern Recognit Lett, vol. 145, pp. 171–177, May 2021, doi: 10.1016/j.patrec.2021.02.011.

A. Rai and H. V. Singh, “Machine Learning-Based Robust Watermarking Technique for Medical Image Transmitted Over LTE Network,” Journal of Intelligent Systems, vol. 27, no. 1, pp. 105–114, Jan. 2018, doi: 10.1515/jisys-2017-0068.

F. Meng, H. Peng, Z. Pei, and J. Wang, “A Novel Blind Image Watermarking Scheme Based on Support Vector Machine in DCT Domain,” in 2008 International Conference on Computational Intelligence and Security, IEEE, Dec. 2008, pp. 16–20. doi: 10.1109/CIS.2008.20.

A. Rai and H. V. Singh, “SVM based robust watermarking for enhanced medical image security,” Multimed Tools Appl, vol. 76, no. 18, pp. 18605–18618, Sep. 2017, doi: 10.1007/s11042-016-4215-3.

M. F. Kazemi, M. A. Pourmina, and A. H. Mazinan, “Analysis of watermarking framework for color image through a neural network-based approach,” Complex & Intelligent Systems, vol. 6, no. 1, pp. 213–220, Apr. 2020, doi: 10.1007/s40747-020-00129-4.

R. Mehta, K. Gupta, and A. K. Yadav, “An adaptive framework to image watermarking based on the twin support vector regression and genetic algorithm in lifting wavelet transform domain,” Multimed Tools Appl, vol. 79, no. 25–26, pp. 18657–18678, Jul. 2020, doi: 10.1007/s11042-020-08634-x.

P. Singh and B. Raman, “A secured robust watermarking scheme based on majority voting concept for rightful ownership assertion,” Multimed Tools Appl, vol. 76, no. 20, pp. 21497–21517, Oct. 2017, doi: 10.1007/s11042-016-4006-x.

A. M. Abdelhakim, H. I. Saleh, and A. M. Nassar, “A quality guaranteed robust image watermarking optimization with Artificial Bee Colony,” Expert Syst Appl, vol. 72, pp. 317–326, Apr. 2017, doi: 10.1016/j.eswa.2016.10.056.

C. Wang, X. Wang, C. Zhang, and Z. Xia, “Geometric correction based color image watermarking using fuzzy least squares support vector machine and Bessel K form distribution,” Signal Processing, vol. 134, pp. 197–208, May 2017, doi: 10.1016/j.sigpro.2016.12.010.

S. Behnia, M. Yahyavi, and R. Habibpourbisafar, “Watermarking based on discrete wavelet transform and q -deformed chaotic map,” Chaos Solitons Fractals, vol. 104, pp. 6–17, Nov. 2017, doi: 10.1016/j.chaos.2017.07.020.

C. N. Mooers, “Preventing Software Piracy,” Computer (Long Beach Calif), vol. 10, no. 3, pp. 29–30, Mar. 1977, doi: 10.1109/C-M.1977.217671.

I. A. Ansari and M. Pant, “Multipurpose image watermarking in the domain of DWT based on SVD and ABC,” Pattern Recognit Lett, vol. 94, pp. 228–236, Jul. 2017, doi: 10.1016/j.patrec.2016.12.010.

Ritu Agrawal and M. Sharma, "Medical Image Watermarking Technique in the Application of E- diagnosis Using M-Ary Modulation," Procedia Comput Sci, vol. 85, pp. 648–655, 2016, doi: 10.1016/j.procs.2016.05.249.

N. Mohananthini and G. Yamuna, “Comparison of multiple watermarking techniques using genetic algorithms,” Journal of Electrical Systems and Information Technology, vol. 3, no. 1, pp. 68–80, May 2016, doi: 10.1016/j.jesit.2015.11.009.

T. Huynh-The, O. Banos, S. Lee, Y. Yoon, and T. Le-Tien, “Improving digital image watermarking by means of optimal channel selection,” Expert Syst Appl, vol. 62, pp. 177–189, Nov. 2016, doi: 10.1016/j.eswa.2016.06.015.

Y. AL-Nabhani, H. A. Jalab, A. Wahid, and R. M. Noor, “Robust watermarking algorithm for digital images using discrete wavelet and probabilistic neural network,” Journal of King Saud University - Computer and Information Sciences, vol. 27, no. 4, pp. 393–401, Oct. 2015, doi: 10.1016/j.jksuci.2015.02.002.

P.-T. Yu, H.-H. Tsai, and J.-S. Lin, "Digital watermarking based on neural networks for colour images," Signal Processing, vol. 81, no. 3, pp. 663–671, Mar. 2001, doi: 10.1016/S0165-1684(00)00239-5.

A. Zear, A. K. Singh, and P. Kumar, “A proposed secure multiple watermarking technique based on DWT, DCT and SVD for application in medicine,” Multimed Tools Appl, vol. 77, no. 4, pp. 4863–4882, Feb. 2018, doi: 10.1007/s11042-016-3862-8.

A. Zear, A. K. Singh, and P. Kumar, “Robust watermarking technique using back propagation neural network: a security protection mechanism for social applications,” International Journal of Information and Computer Security, vol. 9, no. 1/2, p. 20, 2017, doi: 10.1504/IJICS.2017.10003597.

M. Vafaei, H. Mahdavi-Nasab, and H. Pourghassem, “A new robust blind watermarking method based on neural networks in wavelet transform domain,” World Appl Sci J, vol. 22, no. 11, pp. 1572–1580, 2013, doi: 10.5829/idosi.wasj.2013.22.11.2828.

D. Li, L. Deng, B. Bhooshan Gupta, H. Wang, and C. Choi, “A novel CNN based security guaranteed image watermarking generation scenario for smart city applications,” Inf Sci (N Y), vol. 479, pp. 432–447, Apr. 2019, doi: 10.1016/j.ins.2018.02.060.

A. K. Singh, B. Kumar, S. K. Singh, S. P. Ghrera, and A. Mohan, “Multiple watermarking technique for securing online social network contents using Back Propagation Neural Network,” Future Generation Computer Systems, vol. 86, pp. 926–939, Sep. 2018, doi: 10.1016/j.future.2016.11.023.

N. Ramamurthy and S. Varadarajan, “Robust Digital Image Watermarking Using Quantization and Back Propagation Neural Network,” 2012.

Y. Zhang, “Blind Watermark Algorithm Based on HVS and RBF Neural Network in DWT Domain,” 2009.

C.-T. Yen and Y.-J. Huang, “Frequency domain digital watermark recognition using image code sequences with a back-propagation neural network,” Multimed Tools Appl, vol. 75, no. 16, pp. 9745–9755, Aug. 2016, doi: 10.1007/s11042-015-2718-y.

M. Ahmadi et al., “ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks,” Oct. 2018. http://arxiv.org/abs/1810.07248

Downloads

Published

23-03-2025

How to Cite

Sambhaji Marutirao Shedole, & V Santhi. (2025). A Comprehensive Study on Digital Watermarking for Security Threats and Research Directions. International Journal of Data Informatics and Intelligent Computing, 4(1), 54–72. https://doi.org/10.59461/ijdiic.v4i1.160

Issue

Section

Regular Issue