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.

Digital Watermarking

Trends in extreme learning machines

Currently, the most successful approach to steganography in empirical objects, such as digital media, is to embed the payload while minimizing a suitably defined distortion function. The design of the distortion is essentially the only task left to the steganographer since efficient practical codes exist that embed near the payload-distortion bound. The practitioner’s goal is to design the distortion to obtain a scheme with a high empirical statistical detectability. In this paper, we propose a universal distortion design called universal wavelet relative distortion (UNIWARD) that can be applied for embedding in an arbitrary domain. The embedding distortion is computed as a sum of relative changes of coefficients in a directional filter bank decomposition of the cover image. The directionality forces the embedding changes to such parts of the cover object that are difficult to model in multiple directions, such as textures or noisy regions, while avoiding smooth regions or clean edges. We demonstrate experimentally using rich models as well as targeted attacks that steganographic methods built using UNIWARD match or outperform the current state of the art in the spatial domain, JPEG domain, and side-informed JPEG domain.

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Universal distortion function for steganography in an arbitrary domain

This paper presents a complete methodology for designing practical and highly-undetectable stegosystems for real digital media. The main design principle is to minimize a suitably-defined distortion by means of efficient coding algorithm. The distortion is defined as a weighted difference of extended state-of-the-art feature vectors already used in steganalysis. This allows us to "preserve" the model used by steganalyst and thus be undetectable even for large payloads. This framework can be efficiently implemented even when the dimensionality of the feature set used by the embedder is larger than 107. The high dimensional model is necessary to avoid known security weaknesses. Although high-dimensional models might be problem in steganalysis, we explain, why they are acceptable in steganography. As an example, we introduce HUGO, a new embedding algorithm for spatial-domain digital images and we contrast its performance with LSB matching. On the BOWS2 image database and in contrast with LSB matching, HUGO allows the embedder to hide 7× longer message with the same level of security level.

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Using high-dimensional image models to perform highly undetectable steganography

This paper presents a reversible or lossless watermarking algorithm for images without using a location map in most cases. This algorithm employs prediction errors to embed data into an image. A sorting technique is used to record the prediction errors based on magnitude of its local variance. Using sorted prediction errors and, if needed, though rarely, a reduced size location map allows us to embed more data into the image with less distortion. The performance of the proposed reversible watermarking scheme is evaluated using different images and compared with four methods: those of Kamstra and Heijmans, Thodi and Rodriguez, and Lee et al. The results clearly indicate that the proposed scheme can embed more data with less distortion.

Reversible Watermarking Algorithm Using Sorting and Prediction

Scheduling divisible loads with the nonlinear computational complexity is a challenging task as the recursive equations are nonlinear and it is difficult to find closed-form expression for processing time and load fractions In this study we attempt to address a divisible load scheduling problem for computational loads having second-order computational complexity in a master-slave paradigm with nonblocking mode of communication First, we develop algebraic means of determining the optimal size of load fractions assigned to the processors in the network using a mild assumption on communication-to-computation speed ratio We use numerical simulation to verify the closeness of the proposed solution Like in earlier works which consider processing loads with first-order computational complexity, we study the conditions for optimal sequence and arrangements using the closed-form expression for optimal processing time Our finding reveals that the condition for optimal sequence and arrangements for second-order computational loads are the same as that of linear computational loads This scheduling algorithm can be used for aerospace applications such as Hough transform for image processing and pattern recognition using hidden Markov model (HMM)

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Scheduling Second-Order Computational Load in Master-Slave Paradigm

A new algorithm is presented for solving ill-conditioned linear equations. This method is radically different from conventional methods. We improve the condition of the linear equation first and then solve the well-conditioned one. The approximate solution is modified to get the exact solution. Numerical simulations show that the proposed method is effective enough to be applied to the practical finite element problems. Mathematical soundness of the algorithm is also shown. Several implementation techniques are discussed. It can be used not only for solving ill-conditioned equations but also for inverse problems and many other areas.

A new algorithm for solving ill conditioned linear systems

An innocuous visual secret sharing scheme over natural images is presented in this paper. Secret sharing scheme allows a group of participants to share a secret (i.e., an image) among them. In case of(k, n) visual cryptography any group of k qualified participants among n (where k les n) can reconstruct the secret. This paper presents (n, n) visual cryptography scheme. This scheme does not apply dithering techniques to hide a secret image. Thus, it does not degrade quality of the secret image and in particular, this scheme is far from negative photo effect. Instead of dithering, this scheme spreads data by applying simple arithmetic operations. The rationale of arithmetic operations is presented.

An Innocuous Visual Cryptography Scheme

Even though state-of-the-art digital cameras are equipped with auto-focusing and motion compensation functions, several other factors including limited contrast, inappropriate exposure time and improper device handling can still lead to unsatisfactory image quality such as blurriness. Indeed, blurry images make up a significant percentage of anyone's picture collections. Consequently, an efficient tool to detect blurry images and label or separate them for automatic deletion in order to preserve storage capacity and the quality of image collections is needed. A new technique for automatic detection and removal of blurry pictures is presented. Initially, a set of interest points and local image areas is extracted. These areas are then evolved in time according to the conventional linear scale space. The gradient of the evolution curve through scale is then used to produce a 'blur graph' representing the probability of a picture being blurred or not. Complexity is kept low by applying a Monte-Carlo like technique for the selection of representative image areas and interest points and by implicitly estimating the gradient of the scale-space curve evolution. An exhaustive evaluation of the proposed technique is conducted to validate its performance in terms of detection accuracy and efficiency.

Hyoung Joong Kim

Papers

Scheduling Second-Order Computational Load in Master-Slave Paradigm

A new algorithm for solving ill conditioned linear systems

An Innocuous Visual Cryptography Scheme

Linear Gaussian blur evolution for detection of blurry images

Linear collaborative discriminant regression classification for face recognition