As an alternative implementation of Slepian-Wolf coding, distributed arithmetic coding is very competitive in short and medium block lengths. The existing distributed arithmetic coding decoder uses the maximum a posteriori metric and the M-algorithm to select the decoding sequence by considering the prior information. Since the prior probability distribution has been explored by the encoding process via the model stage and retained in the codeword, we propose a modified decoding metric ignoring the prior information. The reliability of the decoding paths is only determined by the correlation between the side information and the input source. Simulation results show that the modified metric can significantly reduce the decoding error.

The use of human biometrics is becoming widespread and its major application is human recognition for controlling unauthorized access to both digital services and physical localities. However, the practical deployment of human biometrics for recognition poses a number of challenges, such as template storage capacity, computational requirements, and privacy of biometric information. These challenges are important considerations, in addition to performance accuracy, especially for authentication systems with limited resources. In this paper, we propose a wave atom transform (WAT)-based palm-vein recognition scheme. The scheme computes, maintains, and matches palm-vein templates with less computational complexity and less storage requirements under a secure and privacy-preserving environment. First, we extract palm-vein traits in the WAT domain, which offers sparser expansion and better capability to extract texture features. Then, the randomization and quantization are applied to the extracted features to generate a compact, privacy-preserving palm-vein template. We analyze the proposed scheme for its performance and privacy-preservation. The proposed scheme obtains equal error rates (EERs) of 1.98%, 0%, 3.05%, and 1.49% for PolyU, PUT, VERA and our palm-vein datasets, respectively. The extensive experimental results demonstrate comparable matching accuracy of the proposed scheme with a minimum template size and computational time of 280 bytes and 0.43 s, respectively.

The design of unified, efficient, and lightweight cryptographic platform for resource-constrained on-board devices such as sensors, microcontrollers, and actuators in the context of Internet of Vehicles remains an open and challenging problem, for both academic and industry. Elliptic curve cryptography (ECC) is considered as a promising encryption algorithm for the next generation communications, as it could provide the same strong security level using relatively smaller key size when compared to the currently used Rivest–Shamir–Adleman algorithm. However, traditional ECCs have the disadvantage of using a fixed curve, making it very easy to be intensively analyzed while being hard to construct a united platform for on-board devices with processors of different instruction lengths. To mitigate the above problem, this paper suggests a dynamic scalable elliptic curve cryptosystem. To synchronize the curve in use, a curve list of different security levels is generated and preserved on both parties. Since both parties randomly choose the curve and the prime number, a extra security level could be provided, so that the security level can still remain the same even using smaller key sizes, while the computation efficiency will be enhanced and the power consumption will be reduced, which is especially suitable for the application in on-board embedded devices. Detailed experimental results illustrate that the presented scheme improves the efficiency by 30% in average when compared with traditional ECC implementations on a similar security level. Therefore, the proposed scalable ECC scheme as a unified cryptographic platform is more economic for these on-board devices in vehicles.

Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

In this paper, an enhanced perceptual image authentication approach is proposed with extra ability of tamper localization and image self-restoration by combining perceptual hashing and digital watermarking technologies. Compared with other perceptual hashing schemes, this proposed approach could locate the maliciously tampered regions and further recover these regions to some extent. Another advantage of this approach is its robustness to various non-malicious image processing operations. This approach could provide better robustness to most content-based image processing operations such as JPEG compression and additive Gaussian noises than most existing semi-fragile watermarking methods. Experimental results demonstrated the high authentication accuracy rate to non-malicious and malicious image processing operations. Moreover, maliciously tampered regions could be correctly localized and the original images can be recovered with good quality as well.

The rapid advancement of digital scanner, digital camera and digital video recorder technologies has created a huge market for digital multi-media, such as documents, photos and videos, storage, retrieval and reproduction market. The ease of modifications using editing tools makes the identification of original masterpiece difficult and these forgeries sometimes become an important issue for identification, authentication and law enforcement. The demand of a security protection chip increases as digital documents in form of digital video became popular and forensic approaches for detecting forgery evident became vital. In this paper, a real time forensic chip is designed and implemented. This Chip captures real time video frames and encodes forensic mark into the video frame. The video frame with forensic mark embedded is then transmitted to remote host through TCP/IP. On the remote host, the same Chip can be used to decode the stored forensic mark from the received video frame. Any modifications to the original captured video can immediately be detected by the chip in real time.

Based on curvelet transform this paper embeds one leveled watermark onto significant coefficients by quantization in individual frequency scales. Watermark bits come from image HASH, which is defined in the paper. Bigger coefficients are used for watermarking for the conception that coefficient energy is proportion to its directional sensitivity and bigger coefficients are more stable. Watermark is extracted without resort to the original image. The proposed watermarking can realize multifunction of content authentication and copyright protection the same time.

A subset of genes sharing compatible expression patterns under a subset of conditions can be found from DNA microarray data using biclustering algorithms. In this paper, we present a novel geometrical biclustering algorithm in combination with gene ontology annotations to identify the gene functional biclusters. Unlike many existing biclustering algorithms, we first consider the biclustering patterns through geometrical interpretation. Such a perspective makes it possible to unify the formulation of different types of biclusters as hyperplanes in spatial space and facilitates the use of a generic plane finding algorithm for bicluster detection. In our bottom-up biclustering algorithm, the well-known Hough transform is first employed in pair-column spaces to reduce the computation complexity and then the resulting patterns are merged step by step into large-size biclusters incorporated with gene functional modules. The algorithm integrates the numerical characteristics in a gene expression matrix and the gene functions in the biological activities. Our experiments on real data show that the new algorithm outperforms most existing methods for mining gene functional biclusters.

BACKGROUND: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular to solve this type of problems. These models show good performance in accommodating noise, variability and low replication of microarray data. However, the correlation between different fluorescent signals measured from a gene spot is ignored, which can diversely affect the data analysis step. In fact, the intensities of the two signals are significantly correlated across samples. The larger the log-transformed intensities are, the smaller the correlation is. RESULTS: Motivated by the complicated error relations in microarray data, we propose a multivariate hierarchical Bayesian framework for data analysis in the replicated microarray experiments. Gene expression data are modelled by a multivariate normal distribution, parameterized by the corresponding mean vectors and covariance matrixes with a conjugate prior distribution. Within the Bayesian framework, a generalized likelihood ratio test (GLRT) is also developed to infer the gene expression patterns. Simulation studies show that the proposed approach presents better operating characteristics and lower false discovery rate (FDR) than existing methods, especially when the correlation coefficient is large. The approach is illustrated with two examples of microarray analysis. The proposed method successfully detects significant genes closely related to the experimental states, which are verified by the biological information. CONCLUSIONS: The multivariate Bayesian model, compatible with the dependence between mean and variance in the univariate Bayesian model, relaxes the constant coefficient of variation assumption between measurements by adding a covariance structure. This model improves the identification of differentially expressed genes significantly since the Bayesian model fit well with the microarray data.

Multipurpose watermarking for content authentication and copyright verification are accomplished by using the multiscale curvelet transform. A curvelet transform gains better and sparser representation than most traditional multiscale transforms. In this paper, an image is decomposed into multiscale coefficients with a dyadic number of wedges constructed from a variety of neighboring scales. Image hash is designed to extract image features from an approximate scale. The image features represented in the form of bit sequences are then embedded onto the wedges by a quantization based on human visual system behavior. The implementation strategy achieves content authentications for fatigue watermarking and copyright verifications for robust watermarking. The experiments demonstrate good results to support the feasibility of using this method in multipurpose applications.

In this paper, a novel image hiding scheme based on block difference is proposed to hide a secret-image into a cover-image such that an unintended observer will not be aware of the hidden secret-image. By exploring the property of block difference between the cover-image and the secret-image, the image quality of the resulting stego-image can be greatly improved. Experimental results demonstrate that the resulting stego-image has high peak signal-to-noise ratio and the visual quality of the stego-image is indistinguishable from the original cover-image.

In this paper, we propose a novel digital watermarking to hide a trademark into a host-image such that an unintended observer will not be aware of the watermarking. By inserting the watermarking information into the random positioning location of a DCT image, the image capability against median filtering can be greatly improved. Experimental results demonstrate that the resulting proposed digital watermarking scheme has a higher peak signal-to-noise ratio and the visual quality of the digital watermarked image is indistinguishable from the original unmarked image.

Based on the construction of linear codes proposed by Niederriter and Xing (see Applicable Algebra in Engineering, Communication and Computing, vol.10, p.425-32, 2000), a corresponding generator matrix is derived. The duality operator and the propagation operator of these extended linear codes are studied. A sufficient and necessary condition under which these two operators are commutative is obtained. This commutative property is found useful for constructing the corresponding parity-check matrix and, thus, the error detection/recovery codes.

We first present a modified Hopfield network, the clipped Hopfield network, with synaptic weights assigned to three values {-1,0,+1}. We give the necessary conditions under which a set of 2n binary vectors can be stored as stable points of the network. We show that in the parallel updating mode, for most of the state vectors, the network will always converge to these 2n stable points. We further demonstrate that these 2n stable points can be divided into two groups, the alpha group and the beta group, each with n stable points. It is shown that the basins of attraction of the stable points in the alpha group are evenly distributed, and the basins of attraction of the stable points in the beta group are also evenly distributed. By ways of application, we show that this class of Hopfield network can be used to build a cryptographically secure keystream generator.

Efficient adaptive image compression using a structured artificial neural network (ANN) is described. An image is first divided into a series of sub blocks with size 8/spl times/8 pixels. Then each of them is transformed by a discrete cosine transform (DCT) using a structured ANN. Then, all the sub blocks are sorted into 4 classes using another layer of structured ANN, according to their level of activity within each sub block. Adaptivity is provided by assigning bits between classes. The neural network used is a structured one instead of a fully connected one, so that convergency and speed of learning are dramatically improved. Each subnetwork is trained and tested independently. Excellent performance is achieved, in comparison to traditional fully connected neural network image compression methods.

We propose a dynamic text compression technique with a back searching algorithm and a new storage protocol. Codes being encoded are divided into three types namely copy, literal and hybrid codes. Multiple dictionaries are adopted and each of them has a linked sub-dictionary. Each dictionary has a portion of pre-defined words i.e. the most frequent words and the rest of the entries will depend on the message. A hashing function developed by Pearson (1990) is adopted. It serves two purposes. Firstly, it is used to initialize the dictionary. Secondly, it is used as a quick search to a particular word. By using this scheme, the spaces between words do not need to be considered. At the decoding side, a space character will be appended after each word is decoded. Therefore, the redundancy of space can also be compressed. The result shows that the original message will not be expanded even if we have poor dictionary design.