Automatic Selection of GA Parameters for Fragile Watermarking

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• 作者：Marco Botta (15)
  Davide Cavagnino (15)
  Victor Pomponiu (16)
  
• 关键词：Information hiding ; Fragile watermarking ; Genetic algorithms ; Karhunen ; Lo猫ve Transform
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：1
• 期：1
• 页码：526-537
• 全文大小：723 KB
• 参考文献：1. Aslantas, V., Ozer, S., Ozturk, S.: Improving the performance of DCT-based fragile watermarking using intelligent optimization algorithms. Optics Communications 282(14), 2806鈥?817 (2009) CrossRef
  2. Barreto, P.S.L.M., Kim, H.Y., Rijmen, V.: Toward secure publickey blockwise fragile authentication watermarking. In: IEE Proceedings - Vision, Image and Signal Processing 2002, vol. 148(2), pp. 57鈥?2 (2002)
  3. Botta, M., Cavagnino, D., Pomponiu, V.: KL-F: Karhunen-Lo猫ve Based Fragile Watermarking. In: 5th International Conference on Network and System Security NSS 2011, pp. 65鈥?2 (2011)
  4. Botta, M., Cavagnino, D., Pomponiu, V.: Fragile watermarking using Karhunen-Lo猫ve transform: the KLT-F approach. Accepted for publication in Soft Computing, Springer (2014)
  5. Cox, I.J., Miller, M.L., Bloom, J.A., Fridrich, J., Kalker, T.: Digital Watermarking and Steganography, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2008)
  6. D铆az, D.S., Romay, M.G.: Introducing a watermarking with a multi-objective genetic algorithm. In: Proceedings of the 2005 conference on Genetic and evolutionary computation (GECCO), pp. 2219鈥?220 (2005)
  7. Gonzalez, R.C., Wintz, P.: Digital Image Processing, 2nd ed. Addison-Wesley Publishing Company (1987)
  8. Huang, H.-C., Chu, C.-M., Pan, J.-S.: The optimized copyright protection system with genetic watermarking. Soft Computing 13(4), 333鈥?43 (2009) CrossRef
  9. Lee, S.-K., Ho, Y.-S.: Fragile watermarking scheme using a simple genetic algorithm. In: International Conference on Consumer Electronics, pp. 190鈥?91 (2002)
  10. Li, L.-J., Wang, G., Fei-Fei, L.: OPTIMOL: automatic Object Picture collecTion via Incremental MOdel Learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1鈥? (2007)
  11. Lu, Yinghua, Han, Jialing, Kong, Jun, Yang, Yulong, Hou, Gang: A Novel Color Image Watermarking Method Based on Genetic Algorithm and Hybrid Neural Networks. In: Greco, Salvatore, Hata, Yutaka, Hirano, Shoji, Inuiguchi, Masahiro, Miyamoto, Sadaaki, Nguyen, Hung Son, S艂owi艅ski, Roman (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 806鈥?14. Springer, Heidelberg (2006) CrossRef
  12. Shieh, C.-S., Huang, H.-C., Wang, F.-H., Pan, J.-S.: Genetic watermarking based on transform-domain techniques. Pattern Recognition 37(3), 555鈥?65 (2004) CrossRef
  13. Shih, F.Y., Wu, Y.-T.: Robust watermarking and compression for medical images based on genetic algorithms. Information Sciences 175(3), 200鈥?16 (2005) CrossRef
  14. Shih, F.Y., Wu, Y.-T.: Enhancement of image watermark retrieval based on genetic algorithms. Journal of Visual Communication and Image Representation 16(2), 115鈥?33 (2005) CrossRef
  15. Usman, I., Khan, A., Chamlawi, R., Majid, A.: Image Authenticity and Perceptual Optimization via Genetic Algorithms and a Dependence Neighborhood. International Journal of Applied Mathematics and Computer Sciences 4(1), 37鈥?2 (2007)
  16. Wang, R.-Z., Lin, C.-F., Lin, J.-C.: Image hiding by optimal LSB substitution and genetic algorithm. Pattern Recognition 34(3), 671鈥?83 (2001) CrossRef
  17. Wu, Ming-Ni, Lin, Min-Hui, Chang, Chin-Chen: A LSB Substitution Oriented Image Hiding Strategy Using Genetic Algorithms. In: Chi, Chi-Hung, Lam, Kwok-Yan (eds.) AWCC 2004. LNCS, vol. 3309, pp. 219鈥?29. Springer, Heidelberg (2004) CrossRef
  
• 作者单位：Marco Botta (15)
  Davide Cavagnino (15)
  Victor Pomponiu (16)
  
  15. Dipartimento di Informatica, Universit脿 degli Studi di Torino, Corso Svizzera 185, 10149, Torino, Italy
  16. Department of Radiology, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, 15213, PA, USA
  
• ISSN：1611-3349

文摘

Genetic Algorithms (GAs) are known to be valuable tools for optimization purposes. In general, GAs can find good solutions by setting their configuration parameters, such as mutation and crossover rates, population size, etc., to standard (i.e., widely used) values. In some application domains, changing the values of these parameters does not improve the quality of the solution, but might influence the ability of the algorithm to find such solution. In other application domains, fine tuning these parameters could result into a significant improvement of the solution quality. In this paper we present an experimental study aimed at finding how fine tuning the parameters of a GA used for the insertion of a fragile watermark into a bitmap image influences the quality of the resulting digital object. However, when proposing a GA based new tool to non-expert users, selecting the best parameter setting is not an easy task. Therefore, we will suggest how to automatically set the GA parameters in order to meet the quality and/or running time performances requested by the user.