LRW-CRDB: Lossless Robust Watermarking Scheme for Categorical Relational Databases

In 2002, Agrawal and Kiernan defined six basic requirements, including preventing illegal watermark embedding and authentication, reversibility, robustness, and others, which must be satisfied when a reversible watermark is designed for relational databases. To meet these requirements, in this paper, a lossless watermarking scheme for a categorical relational database called LRW-CRDB (lossless robust watermarking for categorical relational databases) is proposed. In our LRW-CRDB scheme, the database owner needs to generate two secret embedding keys, K1 and K2, in advance. Then, two reference sets are generated based on two different secret embedding keys and a symmetry-based data hiding strategy, and then these are used for the watermark embedding phases. Experimental results confirmed that our LRW-CRDB scheme successfully detects 100% of hidden watermarks, even when more than 95% of the watermarked relational database has been deleted. In other words, the robustness of our proposed LRW-CRDB scheme outperforms other existing schemes under a variety of possible attacks, such as alteration, sorting, deletion, and mix-match attacks.

Lossless Watermarking Algorithm Based on Multiple-Base Notational System and Pixel Difference

In order to identify the authenticity of the industrial packaging, the solution to the current watermark technology is mainly to embed the same watermark capacity into each pixel value in the image, so that the watermark capacity is small, and the anti-distortion performance is not good. Data watermarking algorithm based on system and pixel difference. First divide the carrier image into non-overlapping sub-blocks, and calculate the pixel difference of each sub-block; introduce a hybrid system to obtain different bases. According to the pixel difference, the base selection rule is defined; considering the human visual characteristics, the pixel interval adaptive adjustment mechanism is designed to improve the coding technology and complete the watermark information embedding; finally, the watermark information extraction mechanism is established to restore the watermark. The experimental results show that compared with the current data watermarking algorithm, the proposed algorithm has ideal imperceptibility, as well as larger watermark capacity and lower distortion.

WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication

Traditional watermarking techniques extract the watermark from a suspected image, allowing the copyright information regarding the image owner to be identified by the naked eye or by similarity estimation methods such as bit error rate and normalized correlation. However, this process should be more objective. In this paper, we implemented a model based on deep learning technology that can accurately identify the watermark copyright, known as WMNet. In the past, when establishing deep learning models, a large amount of training data needed to be collected. While constructing WMNet, we implemented a simulated process to generate a large number of distorted watermarks, and then collected them to form a training dataset. However, not all watermarks in the training dataset could properly provide copyright information. Therefore, according to the set restrictions, we divided the watermarks in the training dataset into two categories; consequently, WMNet could learn and identify the copyright information that the watermarks contained, so as to assist in the copyright verification process. Even if the retrieved watermark information was incomplete, the copyright information it contained could still be interpreted objectively and accurately. The results show that the method proposed by this study is relatively effective.

Withdrawal Notice: Lossless Watermarking Algorithm Based on Multiple- Base Notational System and Pixel Difference

The article has been withdrawn at the request of the authors and editor of the journal Recent Advances in Computer Science and Communications. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Lossless Watermarking for Image Ratification: An Implementation based on 12th Root of Exponential Function

In this article, an exponential transformation based digital image watermarking scheme has been described. The method is employed with 12th root of exponential function for embedding watermark into the cover image. Due to bulk data capacity of the image, many watermarking techniques uses more time for data insertion and separation processes. Algorithm is designed in such a way to embed a watermark of 1/16th size of host. The retrieved watermark is examined under various security analysis. Many standard test images are subjected for watermarking process under proposed scheme resulted with better scale and utilizes less computational time compared to other techniques.