A New Scheme of Neural Network and DCT-Domain Based Digital Watermarking

2014 ◽  
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
Dct Domain

A DCT-Domain System for Digital Watermarking

2013 ◽  
Vol 321-324 ◽  
pp. 2609-2612
Author(s):  
Yan Liang ◽  
Gao Yan ◽  
Chun Xia Qi
Keyword(s):  
Signal Processing ◽  
Digital Watermarking ◽  
Copyright Protection ◽  
Random Sequence ◽  
Multimedia Data ◽  
Dct Domain ◽  
Digital Document ◽  
Dct Coefficients ◽  
Processing Techniques ◽  
Signal Processing Techniques

Digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia data in a networked environment. It makes possible to tightly associated to a digital document a code allowing the identification of the data creator, owner, authorized consumer, and so on. In this paper a new DCT-domain system for digital watermarking algorithm for digital images is presented: the method, which operates in the frequency domain, embeds a pseudo-random sequence of scrambled image in a selected set of DCT coefficients. After embedding, the watermark is adapted to the image by exploiting the masking characteristics of the human visual system, thus ensuring the watermark invisibility. By exploiting the statistical properties of the embedded sequence, the mark can be reliably extracted without resorting to the original uncorrupted image. Experimental results demonstrate that the watermark is robust to several signal processing techniques, including JPEG compression, cut, fuzzy, addition of noise, and sharpen.

Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services

2012 ◽  
pp. 106-121
Author(s):  
Kensuke Naoe ◽  
Hideyasu Sasaki ◽  
Yoshiyasu Takefuji
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Digital Watermarking ◽  
Visual Features ◽  
Learning Technology ◽  
Key Generation ◽  
Visual Objects ◽  
Training Approach ◽  
Intelligent Information ◽  
Mobile Collaboration

The Service-Oriented Architecture (SOA) demands supportive technologies and new requirements for mobile collaboration across multiple platforms. One of its representative solutions is intelligent information security of enterprise resources for collaboration systems and services. Digital watermarking became a key technology for protecting copyrights. In this article, the authors 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 in the forms of visual features out of visual objects or data as training data set for machine learning of digital watermark. Second, the proposed method of watermark extraction is processed by presenting visual features of the target visual image into extraction key or herein is a classifier generated in advance by the training approach of machine learning technology. Third, the training approach is to generate the extraction key, which is conditioned to generate watermark signal patterns, only if proper visual features are presented to the classifier. In the proposed method, this 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. In the experiments, they have shown that our proposed method is robust to high pass filtering and JPEG compression. The proposed method is limited in its applications on the positions of the feature sub-blocks, especially on geometric attacks like shrinking or rotation of the image.

Digital Watermarking Based on Neural Network Technology for Grayscale Images

2011 ◽  
pp. 204-212
Author(s):  
Jeanne Chen ◽  
Tung-Shou Chen ◽  
Keh-Jian Ma ◽  
Pin-Hsin Wang
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
The Internet ◽  
Network Technology ◽  
Traditional Methods ◽  
Internet Environment ◽  
Network Technologies ◽  
Data Passing ◽  
Neural Network Technology ◽  
Grayscale Images

Great advancements made on information and network technologies have brought on much activity on the Internet. Traditional methods of trading and communication are so revolutionized that everything is quasi-online. Amidst the rush to be online emerge the urgent need to protect the massive volumes of data passing through the Internet daily. A highly dependable and secure Internet environment is therefore of utmost importance.

A STUDY OF DIGITAL WATERMARKING RECOGNITION USING ORTHOGONAL CODE SEQUENCES WITH A BACK-PROPAGATION NEURAL NETWORK

2013 ◽  
Vol 37 (3) ◽  
pp. 459-465
Author(s):  
Chih-Ta Yen ◽  
Ing-Jr Ding ◽  
Zong-Wei Lai
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
Median Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Interference ◽  
Low Pass ◽  
Neural Network Algorithm

Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh–Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First method, we used a low-pass filter and median filter to remove noise interferences. The second one, we used a back-propagation neural network algorithm to suppress noises. We removed nearly all noise and recovered the originally embedded watermarks of Walsh–Hadmard codes.

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