Jeng-Shyang Pan

Bio: Jeng-Shyang Pan is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 50, co-authored 789 publications receiving 11645 citations. Previous affiliations of Jeng-Shyang Pan include National Kaohsiung Normal University & Technical University of Ostrava.

Novel Reversible Data Hiding Scheme for AMBTC-Compressed Images by Reference Matrix

Abstract: This paper proposes a novel reversible data hidng scheme in images compressed by absolute moment block truncation coding (AMBTC). In this scheme, the secret data is embedded into the quantization levels of each AMBTC-compressed image block based on a reference matrix. Original quantization levels are transformed into another watermarking message which will combine with bitmap in each image block. The reconstructed image quality is exactly the same as the original AMBTC-compressed version due to the reversibility. Extensive experimental results demonstrate the effectiveness of the proposed scheme and good image quality of the embedded image.

A competitive mechanism based multi-objective differential evolution algorithm and its application in feature selection

Abstract: A large number of evolutionary algorithms have been introduced for multi-objective optimization problems in the past two decades. However, the compromise of convergence and diversity of the non-dominated solutions is still the main difficult problem faced by optimization algorithms. To handle this problem, an efficient competitive mechanism based multi-objective differential evolution algorithm (CMODE) is designed in this work. In CMODE, the rank based on the non-dominated sorting and crowding distance is first adopted to create the leader set, which is utilized to lead the evolution of the differential evolution (DE) algorithm. Then, a competitive mechanism using the shift-based density estimation (SDE) strategy is employed to design a new mutation operation for producing offspring, where the SDE strategy is beneficial to balance convergence and diversity. Meanwhile, two variants of the CMODE using the angle competitive mechanism and the Euclidean distance competitive mechanism are proposed. The experimental results on three test suites show that the proposed CMODE performs better than six state-of-the-art multi-objective optimization algorithms on most of the twenty benchmark functions in terms of hypervolume and inverted generation distance. Furthermore, the proposed CMODE is applied to the feature selection problem. The comparison results on feature selection also demonstrate the efficiency of our proposed CMODE.

Privacy Preserving Association Rule Mining Using Binary Encoded NSGA-II

Abstract: When people utilize data mining techniques to discover useful knowledge behind a large database; they also have the requirement to preserve some information so as not to be mined out, such as sensitive or private association rules, classification tree and the like. A feasible way to address this problem is to sanitize the database to conceal sensitive information. In this paper, we focus on privacy preserving in association rule mining. In light of the tradeoff within the side effects accompanying the hiding process, we tackle this problem from a point view of multi-objective optimization. A novel association rule hiding approach was proposed based on evolutionary multi-objective optimization (EMO) algorithm. The binary encoding scheme was adopted in the EMO algorithm. Three side effects, including sensitive rules not hidden, non-sensitive lost rules and spurious rules were formulated as objectives to be minimized. The NSGA II algorithm, a well established EMO algorithm, was utilized to find a suitable subset of transactions to modify by removing items so that the three side effects are minimized. Experiment results were reported to show the effectiveness of the proposed approach.

High Capacity Watermark Embedding Based on Invariant Regions of Visual Saliency

Abstract: A new image watermarking scheme is presented to achieve high capacity information hiding and geometric invariance simultaneously. Visually salient region is introduced into watermark synchronization. The saliency value of a region is used as the quantitative measure of robustness, based on which the idea of locally most salient region (LMSR) is proposed to generate the disjoint invariant regions. A meaningful binary watermark is then encoded using Chinese Remainder Theorem (CRT) in transform domain. Simulation results and comparisons demonstrate the effectiveness of the proposed scheme.

Mean shift: a robust approach toward feature space analysis

Abstract: A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Digital Watermarking

Abstract: Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

PORs: Proofs of Retrievability for Large Files

Abstract: In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.