Manoj Kumar Pandey

Deep artificial neural network based blind color image watermarking

• Authors Details :  
• Sushma Jaiswal,  
• Manoj Kumar Pandey

Journal title : Springer Tracts in Human-Centered Computing

Publisher : Springer Nature Singapore

Online ISSN : 2662-6934

Page Number : 101-112

480 Views Conference

Digital data is growing enormously as the year passes and therefore there is a need of mechanism to protect the digital contents. Image watermarking is one of the important tools for the human to provide copyright protection and authorship. For achieving the ideal balance between imperceptibility and robustness, a robust blind color image watermarking employing deep artificial neural networks (DANN), LWT and the YIQ color model has been presented. In the suggested watermarking method, an original 512-bit watermark is applied for testing and a randomly generated watermark of the same length is used for training. PCA is used to extract 10 statistical features with significant values out of 18 statistical features, and binary classification is used to extract watermarks here. For the four images Lena, Peppers, Mandril, and Jet, it displays an average imperceptibility of 52.48 dB. For the threshold value of 0.3, it does an excellent job of achieving good balance between robustness and imperceptibility. Except for the gaussian noise, rotation, and average filtering attacks, it also demonstrates good robustness against common image attacks. The results of the experiment demonstrate that the suggested watermarking method outperforms competing methods.

Article DOI & Crossmark Data

DOI : https://doi.org/10.1007/978-981-99-3478-2_10

Article Subject Details

• Computational Mathematics
• Computer Science
• Computer Science Applications

Article Keywords Details

• deep artificial neural network
• YIQ
• Color image
• Image watermarking
• Robust watermarking

Article File

Full Text PDF

Article References

• (1). Hadipour, A., Afifi, R.: Advantages and disadvantages of using cryptography in Steganography. In: 17th International ISC conference on information security and cryptology (ISCISC), 88–94 (2020)
• (2). Biermann, Christopher, J.: Handbook of Pulping and Papermaking (2 edition). Academic Press, San Diego (1996). ISBN 0-12-097362-6
• (3). Kumar, S., Singh, B.K., Yadav, M.: A recent survey on multimedia and database watermarking. Multimedia Tools Appl. 79(27–28), 20149–20197 (2020). https://doi.org/10.1007/s11042-020-08881-y
• (4). Anand, A., Singh, A.K.: An improved DWT-SVD domain watermarking for medical information security. Comput. Commun. 152, 72–80 (2020). https://doi.org/10.1016/j.comcom.2020.01.038
• (5). Liu, S., Pan, Z., Song, H.: Digital image watermarking method based on DCT and fractal encoding. IET Image Process. 11, 815–821 (2017)
• (6). Moosazadeh, M., Ekbatanifard, G.: A new DCT-based robust image watermarking method using teaching-learning-based optimization. J. Inf. Secur. Appl. 47, 28–38 (2019). https://doi.org/10.1016/j.jisa.2019.04.001
• (7). Cedillo-Hernandez, M., Cedillo-Hernandez, A., Garcia-Ugalde, F.J.: Improving dft-based image watermarking using particle swarm optimization algorithm. Mathematics 9(15), 1795 (2021). https://doi.org/10.3390/math9151795
• (8). Ernawan, F., Kabir, M.N.: A block-based RDWT-SVD image watermarking method using human visual system characteristics. Vis. Comput. 36(1), 19–37 (2018). https://doi.org/10.1007/s00371-018-1567-x
• (9). Thanki, R., Kothari, A., Borra, S.: Hybrid, blind and robust image watermarking: RDWT – NSCT based secure approach for telemedicine applications. Multimedia Tools Appl. 80(18), 27593–27613 (2021). https://doi.org/10.1007/s11042-021-11064-y
• (10). Verma, V.S., Jha, R.K., Ojha, A.: Digital watermark extraction using support vector machine with principal component analysis based feature reduction. J Vis Commun. Image Represent 31, 75–85 (2015)
• (11). Islam, M., Roy, A., Laskar, R.H.: SVM-based robust image watermarking technique in LWT domain using different sub-bands. Neural Comput. Appl. 32(5), 1379–1403 (2018). https://doi.org/10.1007/s00521-018-3647-2
• (12). Zear, A., Singh, P.K.: Secure and robust color image dual watermarking based on LWT-DCT-SVD, multimedia tools and applications (2021). https://doi.org/10.1007/s11042-020-10472-w
• (13). Mellimi, S., Rajput, V., Ansari, I.A., Ahn, C.W.: A fast and efficient image watermarking scheme based on Deep Neural Network. Pattern Recogn. Lett. 151, 222–228 (2021)
• (14). Chang, T.-J., Pan, I.-H., Huang, P.-S., Hu, C.-H.: A robust DCT-2DLDA watermark for color images. Multimedia Tools Appl. 78(7), 9169–9191 (2018). https://doi.org/10.1007/s11042-018-6505-4
• (15). Mahto, D.K., Anand, A., Singh, A.K.: Hybrid optimisation-based robust watermarking using denoising convolutional neural network. Soft. Comput. (2022). https://doi.org/10.1007/s00500-022-07155-z
• (16). http://www.imageprocessingplace.com/root_files_V3/image_databases.htm. Accessed 20 Nov 2022
• (17). Abdelhakim, A.M., Abdelhakim, M.: A time-efficient optimization for robust image watermarking using machine learning. Expert Syst. Appl. 100, 197–210 (2018)
• (18). Abdulrahman, A.K., Ozturk, S.: A novel hybrid DCT and DWT based robust watermarking algorithm for color images. Multimedia Tools Appl. 78(12), 17027–17049 (2019). https://doi.org/10.1007/s11042-018-7085-z
• (19). Kang, X., Chen, Y., Zhao, F., Lin, G.: Multi-dimensional particle swarm optimization for robust blind image watermarking using intertwining logistic map and hybrid domain. Soft. Comput. 24(14), 10561–10584 (2019). https://doi.org/10.1007/s00500-019-04563-6
• (20). Sharma, S., Sharma, H., Sharma, J.B.: Artifcial bee colony based perceptually tuned blind color image watermarking in hybrid lwtdct domain. Multimed Tools Appl. 80(12), 18753–21878 (2021)
• (21). Jaiswal, S., Pandey, M.K.: Robust digital image watermarking using LWT and Random-Subspace-1DLDA with PCA based statistical feature reduction. In: 2022 Second International Conference on Computer Science, Engineering and Applications, 1–6. IEEE (2022)

---

More Article by Manoj Kumar Pandey