Nucleophile-Catalyzed Additions to Activated Triple Bonds. Protection of Lactams, Imides, and Nucleosides with MocVinyl and Related Groups

Additions of lactams, imides, (S)-4-benzyl-1,3-oxazolidin-2-one, 2-pyridone, pyrimidine-2,4-diones (AZT derivatives), or inosines to the electron-deficient triple bonds of methyl propynoate, tert-butyl propynoate, 3-butyn-2-one, N-propynoylmorpholine, or N-methoxy-N-methylpropynamide in the presence of many potential catalysts were examined. DABCO and, second, DMAP appeared to be the best (highest reaction rates and E/Z ratios), while RuCl3, RuClCp*(PPh3)2, AuCl, AuCl(PPh3), CuI, and Cu2(OTf)2 were incapable of catalyzing such additions. The groups incorporated (for example, the 2-(methoxycarbonyl)ethenyl group that we name MocVinyl) serve as protecting groups for the above-mentioned heterocyclic CONH or CONHCO moieties. Deprotections were accomplished via exchange with good nucleophiles: the 1-dodecanethiolate anion turned out to be the most general and efficient reagent, but in some particular cases other nucleophiles also worked (e.g., MocVinyl-inosines can be cleaved with succinimide anion). Some structural and mechanistic details have been accounted for with the help of DFT and MP2 calculations.

Subjectively adapted high capacity lossless image data hiding based on prediction errors

This article reports on a lossless data hiding scheme for digital images where the data hiding capacity is either determined by minimum acceptable subjective quality or by the demanded capacity. In the proposed method data is hidden within the image prediction errors, where the most well-known prediction algorithms such as the median edge detector (MED), gradient adjacent prediction (GAP) and Jiang prediction are tested for this purpose. In this method, first the histogram of the prediction errors of images are computed and then based on the required capacity or desired image quality, the prediction error values of frequencies larger than this capacity are shifted. The empty space created by such a shift is used for embedding the data. Experimental results show distinct superiority of the image prediction error histogram over the conventional image histogram itself, due to much narrower spectrum of the former over the latter. We have also devised an adaptive method for hiding data, where subjective quality is traded for data hiding capacity. Here the positive and negative error values are chosen such that the sum of their frequencies on the histogram is just above the given capacity or above a certain quality.

High capacity audio watermarking using the high frequency band of the wavelet domain

This paper proposes a novel high capacity robust audio watermarking algorithm by using the high frequency band of the wavelet decomposition at which the human auditory system (HAS) is not very sensitive to alteration. The main idea is to divide the high frequency band into frames and, for embedding, to change the wavelet samples depending on the average of relevant frame¿s samples. The experimental results show that the method has a very high capacity (about 11,000 bps), without significant perceptual distortion (ODG in [¿1 ,0] and SNR about 30dB), and provides robustness against common audio signal processing such as additive noise, filtering, echo and MPEG compression (MP3).

Reversible and high capacity data hiding in medical images

In this paper we introduce a highly efficient reversible data hiding system. It is based on dividing the image into tiles and shifting the histograms of each image tile between its minimum and maximum frequency. Data are then inserted at the pixel level with the largest frequency to maximize data hiding capacity. It exploits the special properties of medical images, where the histogram of their nonoverlapping image tiles mostly peak around some gray values and the rest of the spectrum is mainlyempty. The zeros (or minima) and peaks (maxima) of the histograms of the image tiles are then relocated to embed the data. The grey values of some pixels are therefore modified.High capacity, high fidelity, reversibility and multiple data insertions are the key requirements of data hiding in medical images. We show how histograms of image tiles of medical images can be exploited to achieve these requirements. Compared with data hiding method applied to the whole image, our scheme can result in 30%-200% capacity improvement and still with better image quality, depending on the medical image content. Additional advantages of the proposed method include hiding data in the regions of non-interest and better exploitation of spatial masking.

High capacity FFT-based audio watermarking

Peer-reviewed

High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewed