Title :
Key generation for static visual watermarking by machine learning
Author :
Naoe, Kensuke ; Sasaki, Hideyasu ; Takefuji, Yoshiyasu
Author_Institution :
Grad. Sch. of Media & Governance, Keio Univ., Fujisawa, Japan
Abstract :
Digital watermarking became a key technology for protecting copyrights. In this paper, we propose a method of key generation scheme for static visual digital watermarking by using machine learning technology, neural network as its exemplary approach for machine learning method. The proposed method is to provide intelligent mobile collaboration with secure data transactions using machine learning approaches, herein neural network approach as an exemplary technology. First, the proposed method of key generation is to extract certain type of bit patterns as training data set for machine learning of digital watermark. Second, the proposed method of watermark extraction is processed by presenting visual features by the training approach of machine learning technology. Third, the training approach is to converge the extraction key as the classifier, which is generated by the machine learning process is used as watermark extraction key. The proposed method is to contribute to secure visual information hiding without losing any detailed data of visual objects or any additional resources of hiding visual objects as molds to embed hidden visual objects.
Keywords :
cryptography; feature extraction; image classification; learning (artificial intelligence); mobile computing; neural nets; watermarking; bit pattern extraction; copyright protection; image classifier; intelligent mobile collaboration; key generation; machine learning; neural network; secure data transaction; static visual digital watermarking; visual information hiding; Cybernetics; Machine learning; Watermarking; Copyright Protection; Digital Watermarking; Key generation; Machine Learning; Neural Network;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212624