An efficient robust watermarking method should be resistant to various distortions, including distortions from image processing and geometric attacks. Geometric attacks are significant challenges for watermarking methods because they destroy the synchronization of the watermark between the embedding side and extracting side. It is a considerable challenge to accomplish watermark synchronization for watermarking methods. To address this challenge, a novel robust watermarking method with synchronization is proposed. At the embedding side, the watermark and the template are embedded to generate the watermarked image. If the watermarked image is attacked, the watermark and template are also distorted. At the extracting side, a template enhanced-extracted network is proposed to achieve watermark synchronization. The template enhanced-extracted network effectively extracts the distorted template from the distorted image. The template-enhanced subnet can indirectly enhance the strength of the distorted template in the distorted image and improve the accuracy of the template-extracted subnet. The visual quality of the watermarked image is guaranteed because there is no need to embed the template with high strength. Then, the attack factor is predicted based on the distorted template. By leveraging this prediction, correct watermark extraction with synchronization is achieved. The experimental results demonstrate that the proposed watermarking method with synchronization yields excellent robustness under image processing, geometric attacks and combined attacks.

Image steganographic methods based on encoder-decoder model with end-to-end network architecture recently have been proposed. However, in steganographic applications, the feature map (called stego matrix) generated by the encoder needs to be rounded as a real stego image for the receiver. The loss of precision by rounding stego matrix leads to the decline in the accuracy of extracted secret messages. The challenge of using end-to-end network to preserve robustness against rounding operation is that it is non-differentiable. In this paper, we propose an anti-rounding image steganography method with separable fine-tuning network architecture which includes the joint training stage (JT-stage) and the separable fine-tuning stage (SF-stage). Firstly, in JT-stage, an embedded generator and a stego matrix extractor are jointly learned without rounding operation. Utilizing concatenation in embedded generator can realistically fuse cover image and secret messages. And the multi-scale fusion block and residual dense block in stego matrix extractor can make secret messages more correctly decoded. Moreover, the discriminator is constructed by generative adversarial nets (GAN) in JT-stage to effectively improve the authenticity and steganalysis security. Then, in SF-stage, the embedded generator is frozen, and the stego matrix is obtained and rounded as a stego image. A stego image extractor is constructed by fine-tuning the layers of the stego matrix extractor to improve the accuracy of message extraction. As the loss will not backpropagate in the embedded generator, the non-differentiability of rounding operation can be offset. Experiments show that the proposed separation fine-tuning network is robust to rounding operation, and effectively reduces the degradation of the image quality and steganalysis performance.

In many reversible data hiding (RDH) methods for halftone images, the traditional embedding process embeds a 1-bit secret message into each embeddable pixel or pattern. To improve the embedding efficiency and payload, we propose an RDH method used in halftone images based on the dynamic embedding states group (DESG), which can embed at least 1 bit of secret messages per embeddable pixel or pattern. First, by exploiting the statistical features of$4 \times 4$patterns and the state sequences in each image, the DESG is constructed dynamically, including$n$embedding states with their state patterns and state sequences. Then, secret messages are encoded by matching the longest common subsequence according to the DESG, which are split into several state sequences. The state sequences are embedded by Markov transitions between these$n$changing state patterns. Finally, reversibility is achieved by recording the DESG as the overhead information in RDH. Experiments show that the construction of DESG can improve the embedding efficiency under the same number of embeddable pixels or patterns, and the visual distortion is also significantly reduced by flipping fewer pixels.

Estimation efficiency is one of key topics in computationally intense optical flow algorithm. Traditional numerical iterative methods are effective at eliminating the high frequency components of the estimation error, while keeping most of low frequency components unchanged. In this paper, we consider the multi-grid based real-time implementation of dense optical flow computation by classical Horn-Schunck model. For this purpose, establishing of the linear set of equation, which is required in linear multi-grid model, is carefully studied, and the overall multi-grid framework is presented. Efficiency and effectiveness of the proposed algorithm is validated by experimental results.

MOTIVATION: For many complex traits/diseases, it is believed that rare variants account for some of the missing heritability that cannot be explained by common variants. Sequencing a large number of samples through DNA pooling is a cost-effective strategy to discover rare variants and to investigate their associations with phenotypes. Overlapping pool designs provide further benefit because such approaches can potentially identify variant carriers, which is important for downstream applications of association analysis of rare variants. However, existing algorithms for analysing sequence data from overlapping pools are limited. RESULTS: We propose a complete data analysis framework for overlapping pool designs, with novelties in all three major steps: variant pool and variant locus identification, variant allele frequency estimation and variant sample decoding. The framework can be used in combination with any design matrix. We have investigated its performance based on two different overlapping designs and have compared it with three state-of-the-art methods, by simulating targeted sequencing and by pooling real sequence data. Results on both datasets show that our algorithm has made significant improvements over existing ones. In conclusion, successful discovery of rare variants and identification of variant carriers using overlapping pool strategies critically depend on many steps, from generation of design matrixes to decoding algorithms. The proposed framework in combination with the design matrixes generated based on the Chinese remainder theorem achieves best overall results. AVAILABILITY: Source code of the program, termed VIP for Variant Identification by Pooling, is available at http://cbc.case.edu/VIP.

MOTIVATION: Inference of identical-by-descent (IBD) probabilities is the key in family-based linkage analysis. Using high-density single nucleotide polymorphism (SNP) markers, one can almost always infer haplotype configurations of each member in a family given all individuals being typed. Consequently, the IBD status can be obtained directly from haplotype configurations. However, in reality, many family members are not typed due to practical reasons. The problem of IBD/haplotype inference is much harder when treating untyped individuals as missing. RESULTS: We present a novel hidden Markov model (HMM) approach to infer the IBD status in a pedigree with many untyped members using high-density SNP markers. We introduce the concept of inheritance-generating function, defined for any pair of alleles in a descent graph based on a pedigree structure. We derive a recursive formula for efficient calculation of the inheritance-generating function. By aggregating all possible inheritance patterns via an explicit representation of the number and lengths of all possible paths between two alleles, the inheritance-generating function provides a convenient way to theoretically derive the transition probabilities of the HMM. We further extend the basic HMM to incorporate population linkage disequilibrium (LD). Pedigree-wise IBD sharing can be constructed based on pair-wise IBD relationships. Compared with traditional approaches for linkage analysis, our new model can efficiently infer IBD status without enumerating all possible genotypes and transmission patterns of untyped members in a family. Our approach can be reliably applied on large pedigrees with many untyped members, and the inferred IBD status can be used for non-parametric genome-wide linkage analysis. AVAILABILITY: The algorithm is implemented in Matlab and is freely available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available on Bioinformatics online.