Wang Xiyu

Bio: Wang Xiyu is an academic researcher from University of Jinan. The author has contributed to research in topics: Encryption & Authentication. The author has an hindex of 3, co-authored 8 publications receiving 19 citations.

A novel 1D chaotic system for image encryption, authentication and compression in cloud

Abstract: To solve the privacy image problem stored in cloud, we propose a compressive sensing (CS) based image compression, authentication, encryption algorithm in cloud, which can implement computation outsourcing to get rid of the expensive storage and computation costs. To achieve these goals, combined with the logistic-tent-sine chaotic map, a logistic-tent-sine chaotic map (LTSS) is proposed for it has larger chaotic ranges and better chaotic behaviors than simple 1D chaotic map such as a logistic chaotic map. Then, the LTSS chaotic system is employed for image protection algorithm in cloud, which contains three parts: data owner, cloud services and data user. The primary role of the data owner part is to compress and encrypt the image while embedding the owner’s Palmprint discriminative and binary features for authentication. The low-frequency part of the image is encrypted using the proposed Binary Data Cyclic Encryption algorithm (BDCE) while the owner’s palmprint features are embedded for authentication of the data owner. The high-frequency data are randomized for compression by the CS and following encrypted by the Double Random Phase Encoding (DRPE). For the cloud services, authentication services depend on the key and palmprint features and the high computational compressed sensing reconstruction is implemented in cloud can reduce the user burden. For data users, a user should provide the correct key and right palmprint, and then obtain an image available from the cloud service. The algorithm has better quality compared with the common compressive sensing based algorithms and robust to noise and occlusion for using the discrete wavelet transform. What is more, theoretical analysis and empirical evaluations show that the proposed algorithm can resist to exhaustive attack, differential attack and classical attacks.

SVD-based image compression, encryption, and identity authentication algorithm on cloud

Abstract: Based on singular value decomposition (SVD), an image compression, encryption, and identity authentication scheme is proposed here. This scheme can not only encrypt image data which would store in the cloud but also implement identity authentication. The authors use the SVD to decompose the image data into three parts: the left singular value matrix, the right singular value matrix, and the singular value matrix. The left singular value matrix and right singular value matrix are not as important as the singular value matrix. They propose a logistic-tent-sine chaotic system to encrypt them. In this scheme, they proposed a novel authentication value calculation algorithm, which can calculate the authentication value according to related data. According to the authentication value calculated from the ciphertext, the algorithm has the perfect authentication performance, so as in the scenarios if the image is cropped or added noisy. Theoretical analysis and empirical evaluations show that the proposed system can achieve better compression performance, satisfactory security performance, and low computational complexity.

Palm vein recognition based on competitive code and DPL

Abstract: Dictionary learning is essentially a dimensionality reduction representation of large data sets and always attempts to learn the most pristine features behind the sample. In this paper, the palm vein feature is extracted with the Competitive code, then the Competition code feature of the palm vein is learned by a novel projective Dictionary Pair Learning (DPL) model for pattern classification tasks. Different from conventional Dictionary Learning (DL) methods, which learn a single synthesis dictionary, DPL learns jointly a synthesis dictionary and an analysis dictionary. Such a pair of dictionaries work together to perform representation and discrimination simultaneously. Compared with previous DL methods, DPL employs projective coding, which largely reduces the computational burden in learning and testing. The Competition code is used to extract the direction information of the palm vein. The above two methods are comprehensively applied to the palm veins. This paper uses the multispectral near-infrared palm vein image database of Hong Kong Polytech University for testing. Compared with the palm vein Competition code classification, the recognition rate of the palm vein Competition code feature learned by DPL is improved in some degree. When the palm vein features are extracted with filters in six different directions, the recognition rate of the palm vein Competitive code feature after learning with DPL is increased to 98.96%.

Competition Code And LBP Palm Vein Feature-Level Fusion Using Canonical Correlation Analysis

Abstract: Biometrics is a discipline in computer science that uses biometrics to identify people and control access. With the development of technology, a variety of biometric recognition technologies are widely used, such as palm vein recognition technology. However, there are deficiencies in the feature extraction of the palm vein in one way. It is difficult to classify and identify. Therefore, the palm vein features are extracted in two different ways to obtain two different feature sets. And the two feature sets have complementary characteristics when expressing the palm vein features. Then, to improve the classification effect, we used the Canonical Correlation Analysis (CCA) to fuse the two feature sets. The palm vein features are extracted using a Competition Code and a Local Binary Pattern (LBP) to obtain two different palm vein features sets in this paper. The Competition Code uses the local orientation information of the image to extract the palm vein feature, and the LBP utilizes the local texture feature of the image to extract the palm vein feature. These two features can achieve complementarity. CCA is a feature-level fusion technique. CCA projects two feature sets into the same spatial domain through linear transformation, and achieves effective feature fusion in the same spatial domain. A good classification effect can be achieved by the two feature sets of the CCA fusion palm vein. Our experiments are carried out in a public database of Hong Kong Polytechnic University. Compared with the single palm vein competition code feature or LBP feature, the palm vein feature after CCA fusion shortens the classification time in some degree. And the recognition rate is increased to 98.14% when the ratio of the training sample to the test sample is 9:3.

File compression and encryption based on LLS and arithmetic coding

Abstract: e propose a file compression model based on arithmetic coding. Firstly, the original symbols, to be encoded, are input to the encoder one by one, we produce a set of chaotic sequences by using the Logistic and sine chaos system(LLS), and the values of this chaotic sequences are randomly modified the Upper and lower limits of current symbols probability. In order to achieve the purpose of encryption, we modify the upper and lower limits of all character probabilities when encoding each symbols. Experimental results show that the proposed model can achieve the purpose of data encryption while achieving almost the same compression efficiency as the arithmetic coding.

Towards Integrating Image Encryption with Compression: A Survey

Abstract: As digital images are consistently generated and transmitted online, the unauthorized utilization of these images is an increasing concern that has a significant impact on both security and privacy issues; additionally, the representation of digital images requires a large amount of data. In recent years, an image compression scheme has been widely considered; such a scheme saves on hardware storage space and lowers both the transmission time and bandwidth demand for various potential applications. In this article, we review the various approaches taken to consider joint encryption and compression, assessing both their merits and their limitations. In addition to the survey, we also briefly introduce the most interesting and most often utilized applications of image encryption and evaluation metrics, providing an overview of the various kinds of image encryption schemes available. The contribution made by these approaches is then summarized and compared, offering a consideration of the different technical perspectives. Lastly, we highlight the recent challenges and some potential research directions that could fill the gaps in these domains for both researchers and developers.

A Systematic Review on Physiological-Based Biometric Recognition Systems: Current and Future Trends

Abstract: Biometric deals with the verification and identification of a person based on behavioural and physiological traits. This article presents recent advances in physiological-based biometric multimodalities, where we focused on finger vein, palm vein, fingerprint, face, lips, iris, and retina-based processing methods. The authors also evaluated the architecture, operational mode, and performance metrics of biometric technology. In this article, the authors summarize and study various traditional and deep learning-based physiological-based biometric modalities. An extensive review of biometric steps of multiple modalities by using different levels such as preprocessing, feature extraction, and classification, are presented in detail. Challenges and future trends of existing conventional and deep learning approaches are explained in detail to help the researcher. Moreover, traditional and deep learning methods of various physiological-based biometric systems are roughly analyzed to evaluate them. The comparison result and discussion section of this article indicate that there is still a need to develop a robust physiological-based method to advance and improve the performance of the biometric system.

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Abstract: Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges fac...

Image Encryption Scheme with Compressed Sensing Based on a New Six-Dimensional Non-Degenerate Discrete Hyperchaotic System and Plaintext-Related Scrambling.

Abstract: Digital images can be large in size and contain sensitive information that needs protection. Compression using compressed sensing performs well, but the measurement matrix directly affects the signal compression and reconstruction performance. The good cryptographic characteristics of chaotic systems mean that using one to construct the measurement matrix has obvious advantages. However, existing low-dimensional chaotic systems have low complexity and generate sequences with poor randomness. Hence, a new six-dimensional non-degenerate discrete hyperchaotic system with six positive Lyapunov exponents is proposed in this paper. Using this chaotic system to design the measurement matrix can improve the performance of image compression and reconstruction. Because image encryption using compressed sensing cannot resist known- and chosen-plaintext attacks, the chaotic system proposed in this paper is introduced into the compressed sensing encryption framework. A scrambling algorithm and two-way diffusion algorithm for the plaintext are used to encrypt the measured value matrix. The security of the encryption system is further improved by generating the SHA-256 value of the original image to calculate the initial conditions of the chaotic map. A simulation and performance analysis shows that the proposed image compression-encryption scheme has high compression and reconstruction performance and the ability to resist known- and chosen-plaintext attacks.