400 research outputs found

    A review on medicinal properties of saffron toward major diseases

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    The stigma of Crocus sativus, known as saffron, is one of the most expensive spices in the world. The bioactive components in saffron, picrocrocin, crocin, and safranal, have demonstrated a wide range of uses and capabilities in the medical field. This review is focused on the potential therapeutic applications of saffron on diabetes mellitus (DM), antitumor, anticancer, anti-depressant, Alzheimer’s disease (AD), cardiovascular disease (CVD), erectile dysfunction and antibacterial effects

    A New Digital Watermarking Algorithm Using Combination of Least Significant Bit (LSB) and Inverse Bit

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    In this paper, we introduce a new digital watermarking algorithm using least significant bit (LSB). LSB is used because of its little effect on the image. This new algorithm is using LSB by inversing the binary values of the watermark text and shifting the watermark according to the odd or even number of pixel coordinates of image before embedding the watermark. The proposed algorithm is flexible depending on the length of the watermark text. If the length of the watermark text is more than ((MxN)/8)-2 the proposed algorithm will also embed the extra of the watermark text in the second LSB. We compare our proposed algorithm with the 1-LSB algorithm and Lee's algorithm using Peak signal-to-noise ratio (PSNR). This new algorithm improved its quality of the watermarked image. We also attack the watermarked image by using cropping and adding noise and we got good results as well.Comment: 8 pages, 6 figures and 4 tables; Journal of Computing, Volume 3, Issue 4, April 2011, ISSN 2151-961

    Robust Watermarking using Hidden Markov Models

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    Software piracy is the unauthorized copying or distribution of software. It is a growing problem that results in annual losses in the billions of dollars. Prevention is a difficult problem since digital documents are easy to copy and distribute. Watermarking is a possible defense against software piracy. A software watermark consists of information embedded in the software, which allows it to be identified. A watermark can act as a deterrent to unauthorized copying, since it can be used to provide evidence for legal action against those responsible for piracy.In this project, we present a novel software watermarking scheme that is inspired by the success of previous research focused on detecting metamorphic viruses. We use a trained hidden Markov model (HMM) to detect a specific copy of software. We give experimental results that show our scheme is robust. That is, we can identify the original software even after it has been extensively modified, as might occur as part of an attack on the watermarking scheme

    Subjective and objective watermark detection using a novel approach: Bar-coded watermarking

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    Many digital watermarking algorithms are proposed in the literature. Broadly these watermarking algorithms can be classified into two main categories. The first category of algorithms uses a pseudo random Gaussian sequence (PRGS) watermark whereas the second category of algorithms uses a binary logo as a watermark. The main advantage of PRGS based watermarking scheme is its ability to detect the presence of watermark without manual intervention. However the main drawback is calculating reliable threshold value. In the similar manner the main advantage of binary logo watermark is that there is no need to calculate threshold value but requires manual intervention to detect the presence of watermark. The advantage and disadvantage of either approach is quite clear hence it would be a good idea to design a watermarking scheme which inherits the advantages from both these approaches. In this paper we present one such approach which is termed as bar-code watermarking. The proposed scheme offers objective as well as subjective detection. A PRGS sequence watermark is represented as a bar-code on a binary logo and embedded in the host image. Watermark detection can be either done subjectively or objectively
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