Deep Artificial Neural Network based Blind Color Image Watermarking in YCbCr Color Domain using statistical features
Article Sidebar
Main Article Content
Abstract
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.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
IJCERT Policy:
The published work presented in this paper is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means that the content of this paper can be shared, copied, and redistributed in any medium or format, as long as the original author is properly attributed. Additionally, any derivative works based on this paper must also be licensed under the same terms. This licensing agreement allows for broad dissemination and use of the work while maintaining the author's rights and recognition.
By submitting this paper to IJCERT, the author(s) agree to these licensing terms and confirm that the work is original and does not infringe on any third-party copyright or intellectual property rights.
References
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
-12-097362-6.
Kumar, S., Singh B. K., & Yadav, M. (2020). A Recent Survey on Multimedia and Database Watermarking. Multimedia
Applications (2020) 79:20149–20197.
Anand, A., & Singh, A. K. (2020). An improved DWT-SVD domain watermarking for medical information security. Computer Communication. Tools and 72-80. 47,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 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.