A blind color image watermarking using deep artificial neural network (DANN) in YCbCr color model has been proposed aiming at achieving fair trade-off between imperceptibility and robustness. In the proposed watermarking a random generated watermark of length 512 bit is used for the training purpose and original watermark of length 512 bits is used for the testing. Principal component analysis (PCA) is applied to select the best 10 features out of 18 statistical features. Binary classification is used for watermark extraction. It shows the average imperceptibility of 33.34 dB and average SSIM of 0.9860 for four images Lena, Peppers, Mandril and Jet. It performs well in terms of balancing the imperceptibility and robustness, for the threshold value 32. The proposed scheme takes 7.56 seconds for watermark embedding and extraction. It also shows good robustness against common image attacks including the combination of image attacks except the gaussian noise with intensity 0.06 and cropping 20% attacks. The experiment result shows that the proposed watermarking technique performs well against other technique.
Dr. Sushma Jaiswal,Mr. Manoj Kumar Pandey."Deep Artificial Neural Network based Blind Color Image Watermarking in YCbCr Color Domain using statistical features ". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.10, Issue 03,pp.90-98, March - 2023, URL :https://ijcert.org/ems/ijcert_papers/v100301.pdf,
 Hadipour, A., & Afifi, R. (2020). Advantages and disadvantages of using cryptography in Steganography. 17th International ISC conference on information security and cryptology (ISCISC), 88-94.
 Biermann, & Christopher J. (1996). Handbook of Pulping and Papermaking (2 edition). San Diego, California, USA, Academic Press, ISBN 0-12-097362-6.
 Kumar, S., Singh B. K., & Yadav, M. (2020). A Recent Survey on Multimedia and Database Watermarking. Multimedia Tools and Applications (2020) 79:20149–20197.
 Anand, A., & Singh, A. K. (2020). An improved DWT-SVD domain watermarking for medical information security. Computer Communication. 152, 72-80. https://doi.org/10.1016/j.comcom.2020.01.038.
 Ernawan, F., & Kabir, M. N. (2020). A block-based RDWT-SVD image watermarking method using human visual system characteristics. The visual computer, 36(1), 19-37.
 Thanki, R., Kothari, A. & Borra, S. (2021). Hybrid, blind and robust image watermarking: RDWT–NSCT based secure approach for telemedicine applications. Multimedia Tools and Applications, https://doi.org/10.1007/s11042-021-11064-y
 Cedillo-Hernandez, M., Cedillo-Hernandez, A., & Garcia-Ugalde, F. J. (2021). Improving dft-based image watermarking using particle swarm optimization algorithm. Mathematics, 9(15), 1795. https://doi.org/10.3390/math9151795
 Liu, S. Pan, Z. & Song, H. (2017). Digital Image Watermarking Method Based on DCT and Fractal Encoding. IET Image Process. 11, 815–821.
 Moosazadeh, M. & Ekbatanifard, G. (2019). A new DCT-based robust image watermarking method using teaching-learning-based optimization. Journal of Information Security and Applications. 47, 28-38. https://doi.org/10.1016/j.jisa.2019.04.001
 Verma, V.S., Jha, R.K., & Ojha, A. (2015). Digital watermark extraction using support vector machine with principal component analysis based feature reduction. J Vis Communication Image Represent, 31, 75–85.
 Islam, M., Roy, A. & Laskar, R.H. (2020). SVM-based robust image watermarking technique in LWT domain using different sub-bands. Neural Computing & Application, 32, 1379–1403.
 Zear, A., Singh, P. K., (2021). Secure and robust color image dual watermarking based on LWT-DCT-SVD, multimedia tools and applications. https://doi.org/10.1007/s11042-020-10472-w
 Mellimi, S., Rajput, V., Ansari, I.A., & Ahn, C.W. (2021). A fast and efficient image watermarking scheme based on Deep Neural Network. Pattern Recognition Letters, 151, 222-228.
 Pandey, M.K., Parmar, G., Gupta, R., Sikander, A. (2018). Non-blind Arnold scrambled hybrid image watermarking in YCbCr color space. Microsystem Technologies. https://doi.org/10.1007/s00542-018-4162-1
 Patvardhan, C., Kumar, P., Lakshmi, C.V. (2017). Effective color image watermarking scheme using YCbCrcolor space and QR code. Multimed Tools Appl. DOI 10.1007/s11042-017-4909-1
 Chang, T. J., Pan, I. H., Huang, P. S., & Hu, C. H. (2018). A robust DCT-2DLDA watermark for color images. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-018-6505-4.
 Mahto, D.K., Anand, A. & Singh, A.K. (2022). Hybrid optimisation-based robust watermarking using denoising convolutional neural network. Soft Computing. https://doi.org/10.1007/s00500-022-07155-z
 Abdelhakim, A. M., Abdelhakim, M. (2018). A time-efficient optimization for robust image watermarking using machine learning. Expert Syst Appl 100:197–210.
 Abdulrahman, A. K., Ozturk S (2019) A novel hybrid DCT and DWT based robust watermarking algorithm for color images. Multimed Tools Appl 78(12):17027–17049
 Kang, X., Chen, Y., Zhao, F., Lin, G. (2020). Multi-dimensional particle swarm optimization for robust blind image watermarking using intertwining logistic map and hybrid domain. Soft Computing 24, 10561–10584 (2020). https://doi.org/10.1007/s00500-019-04563-6.
 Sharma, S., Sharma, H., Sharma, J. B. (2021) Artifcial bee colony based perceptually tuned blind color image watermarking in hybrid lwtdct domain. Multimed Tools Appl 80(12):18753–1878.
 Jaiswal, S. and Pandey, M. K., (2022). Robust digital image watermarking using LWT and Random-Subspace-1DLDA with PCA based statistical feature reduction. 2022 Second International Conference on Computer Science, Engineering and Applications, IEEE, 1-6.
 Sharma, S., Sharma, H., Sharma, J. B., Poonia, R. C. (2020). A secure and robust color image watermarking using nature inspired intelligence. Neural Computing and Application. https://doi.org/10.1007/s00521-020-05634-8