Showing papers by "Jeng-Shyang Pan published in 2002"

A VQ-Based Robust Multi-Watermarking Algorithm

Abstract: We propose a new algorithm for digital image watermarking based on the characteristics of vector quantization (VQ). In contrast with the conventional watermark embedding algorithms to embed only one watermark at a time into original source, we present an algorithm to embed multiple watermarks for copyright protection. The embedding and extraction processes are efficient for implementing with the conventional VQ techniques. After embedding, the embedder would output one watermarked reconstruction image and secret keys associated with the embedded watermarks. These secret keys are then registered to the third party to preserve the ownership of the original source in order to prevent attackers from inserting counterfeit watermarks. Simulation results show that if there are some intentional attacks, all the watermarks could survive to protect the copyright. Therefore, we are able to claim robustness, usefulness, and ease of implementation of our algorithm.

A VQ-based robust multi-watermarking algorithm

Abstract: We propose a new algorithm for digital image watermarking based on the characteristics of vector quantization (VQ). In contrast with the conventional watermark embedding algorithms to embed only one watermark at a time into original source, we present an algorithm to embed multiple watermarks for copyright protection. The embedding and extraction processes are efficient for implementing with the conventional VQ techniques. After embedding, the embedder would output one watermarked reconstruction image and secret keys associated with the embedded watermarks. These secret keys are then registered to the third party to preserve the ownership of the original source in order to prevent attackers from inserting counterfeit watermarks. Simulation results show that if there are some intentional attacks, all the watermarks could survive to protect the copyright. Therefore, we are able to claim robustness, usefulness, and ease of implementation of our algorithm.

An Efficient K -Medoids-Based Algorithm Using Previous Medoid Index, Triangular Inequality Elimination Criteria, and Partial Distance Search

Abstract: Clustering in data mining is a discovery process that groups similar objects into the same cluster. Various clustering algorithms have been designed to fit various requirements and constraints of application. In this paper, we study several k-medoids-based algorithms including the PAM, CLARA and CLARANS algorithms. A novel and efficient approach is proposed to reduce the computational complexity of such k-medoids-based algorithms by using previous medoid index, triangular inequality elimination criteria and partial distance search. Experimental results based on elliptic, curve and Gauss-Markov databases demonstrate that the proposed algorithm applied to CLARANS may reduce the number of distance calculations by 67% to 92% while retaining the same average distance per object. In terms of the running time, the proposed algorithm may reduce computation time by 38% to 65% compared with the CLARANS algorithm.

Efficient search approaches for k-medoids-based algorithms

Abstract: In this paper, the concept of previous medoid index is introduced The utilization of memory for efficient medoid search is also presented We propose a hybrid search approach for the problem of nearest neighbor search The hybrid search approach combines the previous medoid index, the utilization of memory, the criterion of triangular inequality elimination and the partial distance search The proposed hybrid search approach is applied to the k-medoids-based algorithms Experimental results based on Gauss-Markov source, curve data set and elliptic clusters demonstrate that the proposed algorithm applied to the CLARANS algorithm may reduce the number of distance calculations from 884% to 952% with the same average distance per object compared with CLARANS The proposed hybrid search approach can also be applied to nearest neighbor searching and the other clustering algorithms

A multi-user based watermarking system with two-security-level keys

Abstract: This paper presents a new watermarking scheme to reduce the detection error probability through removal of local mean values of an original signal. It will be analytically proven that the removal process plays role in reducing the interference between an original signal and a watermark to be embedded. This is simply based on the orthogonality of the DC signal and the AC signal. As a result, the process improves the ability of the right detection. The proposed method is analytically and empirically evaluated with no attack as well as JPEG compression attacks.

An efficient K-medoids-based algorithm using previous medoid index, triangular inequality elimination criteria, and partial distance search

Abstract: Clustering in data mining is a discovery process that groups similar objects into the same cluster. Various clustering algorithms have been designed to fit various requirements and constraints of application. In this paper, we study several k-medoids-based algorithms including the PAM, CLARA and CLARANS algorithms. A novel and efficient approach is proposed to reduce the computational complexity of such k-medoids-based algorithms by using previous medoid index, triangular inequality elimination criteria and partial distance search. Experimental results based on elliptic, curve and Gauss-Markov databases demonstrate that the proposed algorithm applied to CLARANS may reduce the number of distance calculations by 67% to 92% while retaining the same average distance per object. In terms of the running time, the proposed algorithm may reduce computation time by 38% to 65% compared with the CLARANS algorithm.

A multistage VQ based watermarking technique with fake watermarks

Abstract: Digital watermarking is a technology proposed to help address the concern of copyright protection for digital content. To facilitate tracing of copyright violators, different watermarks carrying information about the transaction or content recipient can be embedded into multimedia content before distribution. Such form of "personalised" watermark is called "fingerprint". A powerful attack against digital fingerprinting is the collusion attack, in which different fingerprinted copies of same host data are jointly processed to remove the fingerprints or hinder their detection. This paper first studies a number of existing collusion attack schemes against image fingerprinting. A new collusion attack scheme is then proposed and evaluated, both analytically and empirically. Attack performance in terms of fingerprint detectability and visual quality degradation after attack is assessed. Results obtained from spread spectrum fingerprinting experiments show that the proposed attack can impede fingerprint detection using as few as three fingerprinted images without introducing noticeable visual degradation, hence it is more powerful than those reported in literature. It is also found that increasing the fingerprint embedding strength and spreading factor do not help resist such malicious attacks.