The week's pick
Random Articles
Reseach Article
Survey on Image Watermarking Schemes using Adaptive Soft Computing Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 110 - Number 4 |
| Year of Publication: 2015 |
| Authors: Amita Goel, Anurag Mishra |
10.5120/19302-0751
|
Amita Goel, Anurag Mishra . Survey on Image Watermarking Schemes using Adaptive Soft Computing Techniques. International Journal of Computer Applications. 110, 4 ( January 2015), 4-8. DOI=10.5120/19302-0751
Abstract
During last few years, many soft computing techniques have been employed for image watermarking. These are more into delving the issue of optimization of visual quality of signed images and robustness of the embedding algorithm. The used techniques either operate in adaptive or learning mode, especially those using Artificial Neural Networks or in non adaptive analytical mode such as ones based on Fuzzy logic. Several researchers have also worked on this problem using hybrid and evolutionary algorithms. This research survey especially deals with the image watermarking techniques which rely on adaptive soft computing techniques. The results of gradient descent based Back propagation Network (BPN algorithm, Radial Basis Function Neural Network (RBFNN algorithm and a newly developed Single Layer Feed forward Neural Network (SLFN algorithm commonly known as Extreme Learning Machine (ELM) used to carry out watermarking in uncompressed grayscale images are compared. These techniques are compared for different images and the comparison is based on the visual quality of signed images, the watermark detector response coefficients such as similarity correlation and normalized correlation parameters and the robustness studies. Time complexity issue is also examined to establish the use of watermarking process on a real time scale. It is concluded that the ELM algorithm gives a reasonable generalized behavior in terms of computation of these parameters as compared to its other counterparts. It's fast training in milliseconds and subsequent embedding and extraction makes it suitable for developing watermarking application on a real time scale.
References
- P. Meerwald and A. Uhl, A survey on wavelet domain watermarking algorithms, In Proceedings of SPIE, electronic imaging, security and watermarking of multimedia contents III (2001), (Vol. 4314), 505–51
- N Nikolaidis and I Pitas, Robust image watermarking in the spatial domain, Signal Process (1998), 66(3), 385–403
- J. Hernandez, M. Amado and F. Perez-Gonzalez, DCT-domain watermarking techniques for still images: Detector performance analysis and a new structure, IEEE Transaction on Image Processing (2000), 9(1), 55–67
- Saraju P. Mohanty, K. R. Ramakrishnan, Mohan Kankanhalli, A dual watermarking technique for images, ACM Multimedia 2 (1999), pp. 49–51
- Song Huang, Wei Zhang, Wei Feng, Huaqian Yang, Blind watermarking scheme based on neural network, Seventh World Congress on Intelligent Control and Automation (WCICA 2008), 2008, pp. 5985–5989
- Chuan-Yu Chang and Sheng-Jyun Su, A Neural-Network-Based Robust Watermarking Scheme, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (2005) (Volume 3), 10-12 Oct, 2005, pp 2482-2487
- Cheng-Ri Piao, Seunghwa Beack, Dong-Min Woo, and Seung-Soo Han, A Blind Watermarking Algorithm Based on HVS and RBF Neural Network for Digital Image, ICNC 2006, Part I, LNCS 4221, (2006), pp. 493 – 496
- Zhang Zhi-Ming, Li Rong-Yan and Wang Lei, Adaptive watermark Scheme with RBF Networks, Proceedings of the IEEE International Conference on Neural Networks and Signal Processing (2003) (Volume 2), 14 - 17 Dec. 2003, pp. 1517 - 1520
- Ali Mohammad Latif, An Adaptive Digital Image Watermarking Scheme using Fuzzy Logic and Tabu Search, Journal of Information Hiding and Multimedia Signal Processing (2013), vol 4, pp. 250-271
- Charu Agarwal, Anurag Mishra and Arpita Sharma, Gray-Scale watermarking using GA-BPN Hybrid Network, Journal of Visual Communication and Image Representation, (2013), Vol 24, pp. 1135 - 1146
- G-B Huang, Q-Y Zhu and C K Siew, Extreme Learning Machine: Theory and Applications, (2006), Neurocomputing, vol (70), pp 489-501
- G-B Huang, Q-Y Zhu and C K Siew, Real-Time Learning Capability of Neural Networks, (2006), IEEE Transactions on Neural Networks, vol 17(4), pp 863-878
- G-B Huang (2004), The code for ELM is available on: http://www. ntu. edu. sg/home/egbhuang
- Anurag Mishra, Amita Goel, Rampal Singh, Girija Chetty and Lavneet Singh, A Novel Image Watermarking Scheme Using Extreme Learning Machine, Proceedings of IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, June 10-15, 2012, pp 1-6
- Ingemar Cox, J. Kilian, F. T. Leighton and T, Shamoon, Secure spread spectrum watermarking for multimedia, IEEE Transaction on Image Processing, (1997), vol. 6, 1673–1687
- Rampal Singh, Neelam Dabas, Vikash Chaudhry and Anurag Mishra, On Extreme Learning Machine for Watermarking of Images in Discrete Wavelet Transform Domain, In Proceedings of IEEE 10th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2014), 27-29 August, 2014, Kitakyushu, Japan
- Shwu-Huey Yen and Chia-Jen Wang, SVM Based Watermarking Technique, Tamkang Journal of Science and Engineering (2006) ,Vol. 9, No. 2, pp 141-150
- Bibi Isac and V. Santhi, A study on Digital Image and Video Watermarking using Neural Networks, International Journal of Computer Applications, Vol. 12, No. 9, Jan 2011
- P. Yu, T. Tsai, H. H and Sun D. W, Digital Watermarking of Color Images using Support Vector Machines, National Computer Symposium (2003)
Index Terms
Keywords