Shi-Jinn Horng

A wavelet-tree-based watermarking method using distance vector of binary cluster

This paper proposes a wavelet-tree-based watermarking method using distance vector of binary cluster for copyright protection. In the proposed method, wavelet trees are classified into two clusters using the distance vector to denote binary watermark bits. The two smallest wavelet coefficients in a wavelet tree are used to reduce distortion of a watermarked image. The distance vector, which is obtained from the two smallest coefficients of a wavelet tree, is quantized to decrease image distortion. The trees are classified into two clusters so that they exhibit a sufficiently large statistical difference based on the distance vector, which difference is then used for subsequent watermark extraction. We compare the statistical difference and the distance vector of a wavelet tree to decide which watermark bit is embedded in the embedding process. The experimental results show that the watermarked image looks visually identical to the original and the watermark can be effectively extracted upon image processing attacks.

b-SPECS+: Batch Verification for Secure Pseudonymous Authentication in VANET

The security and privacy preservation issues are prerequisites for vehicular ad hoc networks. Recently, secure and privacy enhancing communication schemes (SPECS) was proposed and focused on intervehicle communications. SPECS provided a software-based solution to satisfy the privacy requirement and gave lower message overhead and higher successful rate than previous solutions in the message verification phase. SPECS also presented the first group communication protocol to allow vehicles to authenticate and securely communicate with others in a group of known vehicles. Unfortunately, we find out that SPECS is vulnerable to impersonation attack. SPECS has a flow such that a malicious vehicle can force arbitrary vehicles to broadcast fake messages to other vehicles or even a malicious vehicle in the group can counterfeit another group member to send fake messages securely among themselves. In this paper, we provide a secure scheme that can achieve the security and privacy requirements, and overcome the weaknesses of SPECS. Moreover, we show the efficiency merits of our scheme through performance evaluations in terms of verification delay and transmission overhead.

A Decision-Theoretic Rough Set Approach for Dynamic Data Mining

Uncertainty and fuzziness generally exist in real-life data. Approximations are employed to describe the uncertain information approximately in rough set theory. Certain and uncertain rules are induced directly from different regions partitioned by approximations. Approximation can further be applied to datamining-related task, e.g., attribute reduction. Nowadays, different types of data collected from different applications evolve with time, especially new attributes may appear while new objects are added. This paper presents an approach for dynamic maintenance of approximations w.r.t. objects and attributes added simultaneously under the framework of decision-theoretic rough set (DTRS). Equivalence feature vector and matrix are defined first to update approximations of DTRS in different levels of granularity. Then, the information system is decomposed into subspaces, and the equivalence feature matrix is updated in different subspaces incrementally. Finally, the approximations of DTRS are renewed during the process of updating the equivalence feature matrix. Extensive experimental results verify the effectiveness of the proposed methods.

An adaptive watermarking scheme for e-government document images

This paper proposes an adaptive watermarking scheme for e-government document images. The adaptive scheme combines the discrete cosine transform (DCT) and the singular value decomposition (SVD) using luminance masking. As a core of masking model in the human visual system (HVS), luminance masking is implemented to improve noise sensitivity. Genetic algorithm (GA), subsequently, is employed for the optimization of the scaling factor of the masking. Involving a number of steps, the scheme proposed through this study begins by calculating the mask of the host image using luminance masking. It is then continued by transforming the mask on each area into all frequencies domain. The watermark image, following this, is embedded by modifying the singular values of DCT-transformed host image with singular values of mask coefficient of host image and the control parameter of DCT-transformed watermark image using Genetic Algorithm (GA). The use of both the singular values and the control parameter respectively, in this case, is not only to improve the sensitivity of the watermark performance but also to avoid the false positive problem. The watermark image, afterwards, is extracted from the distorted images. The experiment results show the improved adaptive performance of the proposed scheme is in resistant to several types of attacks in comparison with the previous schemes; the adaptive performance refers to the adaptive parameter of the luminance masking functioned to improve the performance or robustness of an image from any attacks.

A blind image copyright protection scheme for e-government

An efficient blind copyright protection for e-government document images is proposed through a combination of the discrete cosine transform (DCT) and the singular value decomposition (SVD) based on genetic algorithm (GA). This combination could lead the watermarked image to be resistant to various attacks as well as to improve its performance, security and robustness. DCT, in this case, is applied to the entire image and mapped by a zigzag manner to four areas from the lowest to the highest frequencies. SVD, meanwhile, is applied in each area and then the singular value of DCT-transformed host image, subsequently, is modified in each area with the quantizing value using GA to increase the visual quality and the robustness. The host image is not needed in the watermark extraction and it is more useful than non-blind one in real-world applications. Experiment results demonstrate that the proposed method outperforms other existing methods under several types of attacks.

Cost Analysis of Movement-Based Location Management in PCS Networks: An Embedded Markov Chain Approach

In this paper, we develop an approach of embedded Markov chain to analyze the signaling cost of a movement-based location management (MBLM) scheme. This approach distinguishes itself from those developed in the literature in the following aspects. 1) It considers the location area (LA) architecture used by personal communication service (PCS) networks for location management. 2) It considers two different call handling models that determine after a call whether a location update should be performed. 3) It considers the effect of the call holding time on the call handling models. 4) It proposes to use a fluid flow model to describe the dependence between the cell and the LA residence time. We derive closed-form analytical formulas for the signaling cost, whose accuracy is manifested by a simulation. Based on the analytical formulas, we conduct a numerical study to evaluate the influence of various parameters on the signaling cost. The formulas can contribute to the implementation of the MBLM scheme in PCS networks including Fourth-Generation (4G) Long-Term Evolution. The modeling approach developed in this paper can be exploited to model other location management schemes.

MTPSO algorithm for solving planar graph coloring problem

Research highlights? A modified turbulent particle swarm optimization (MTPSO) model is proposed to solve the planar graph. ? MTPSO combines walking one strategy, assessment strategy and turbulent strategy. ? MTPSO can solve the four-colors problem efficiently and accurately. In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) model for solving planar graph coloring problem based on particle swarm optimization. The proposed model is consisting of the walking one strategy, assessment strategy and turbulent strategy. The proposed MTPSO model can solve the planar graph coloring problem using four-colors more efficiently and accurately. Compared to the results shown in Cui et al. (2008), not only the experimental results of the proposed model can get smaller average iterations but can get higher correction coloring rate when the number of nodes is greater than 30.

A feasible intrusion detector for recognizing IIS attacks based on neural networks

Most activities on the Internet can be recorded as log files of websites and website administrators can inspect log files to locate problems after any network intrusion occurs. However, since log files usually contain a huge quantity of data, without effective methods, it is generally not feasible for administrators to determine the concealed meanings within log files. One method for dealing with this issue is to use neural networks; this is an effective means to distinguish and classify abnormal data in log files, thus alleviating the administrators burden. This paper presents the results of a study on intrusion detection on IIS (Internet information services) utilizing a hybrid intrusion detection system (IDS). The feasibility of the hybrid IDS is validated based on the Internet scanner system (ISS). In the intrusion detection system proposed, we used four different training data sets: 200, 800, 1400, and 2000. The system is trained either by Taguchis experimental design or full factorial experimental design under different training data sets; the former can save much more time than the latter. Under Taguchis experimental design, the best results are obtained when the training data set is of size 1400; overall accuracy in this case is 97.5%. On the contrary, for the full factorial experimental design, the best results are reached when the training data set is of size 2000; overall accuracy is 97.6%. Our study indicates that when to retrain the detector and how much time to allow for this training fully depend on the downgrade percentage of the detection rate, which determines the size of the retraining data set. To reduce the void time for updating the detector, the downgrade percentage should be restricted.

Matrix-based dynamic updating rough fuzzy approximations for data mining

In a dynamic environment, the data collected from real applications varies not only with the amount of objects but also with the number of features, which will result in continuous change of knowledge over time. The static methods of updating knowledge need to recompute from scratch when new data are added every time. This makes it potentially very time-consuming to update knowledge, especially as the dataset grows dramatically. Calculation of approximations is one of main mining tasks in rough set theory, like frequent pattern mining in association rules. Considering the fuzzy descriptions of decision states in the universe under fuzzy environment, this paper aims to provide an efficient approach for computing rough approximations of fuzzy concepts in dynamic fuzzy decision systems (FDS) with simultaneous variation of objects and features. We firstly present a matrix-based representation of rough fuzzy approximations by a Boolean matrix associated with a matrix operator in FDS. While adding the objects and features concurrently, incremental mechanisms for updating rough fuzzy approximations are introduced, and the corresponding matrix-based dynamic algorithm is developed. Unlike the static method of computing approximations by updating the whole relation matrix, our new approach partitions it into sub-matrices and updates each sub-matrix locally by utilizing the previous matrix information and the interactive information of each sub-matrix to avoid unnecessary calculations. Experimental results on six UCI datasets shown that the proposed dynamic algorithm achieves significantly higher efficiency than the static algorithm and the combination of two reference incremental algorithms.

A low cost fragile watermarking scheme in H.264/AVC compressed domain

H.264/AVC-based products have grown tremendously in social networks; issues of content-based authentication become increasingly important. This paper presents a blind fragile watermarking scheme for content-based H.264/AVC authentication, which enjoys high sensitivity to typical video attacks. A spatiotemporal analysis is exploited to guarantee a minimum impact on perceptual quality and bit-rate increment. The watermark features are extracted from intra/inter prediction modes of intra/inter macroblocks, constituting the content-based Message Authentication Code (MAC) which is embedded/extracted in a Group-of-Pictures GOP-based fashion utilizing the syntactic elements of the Network Application Layer (NAL) units from the compressed bitstream. It’s unnecessary to fully decode a compressed bitstream before the embedding or detection processes. A content-based key is generated to control fragile watermark generation, embedding, extraction, and verification algorithms. Additionally, fragility is ensured by selecting the last nonzero quantized ac residuals for watermark embedding. The embedded watermark can be detected and verified by means of partially decoding intra/inter prediction modes from syntactic elements of the bitstream without the prior knowledge of the original video or complete decoding. Experiment results demonstrate that the performance of the proposed scheme is excellent in terms of bit-rate and perceptual quality. Furthermore, various types of content-preserving and/or content-changing attacks can be detected efficiently.