Nikos Nikolaidis

Robust image watermarking in the spatial domain

The rapid evolution of digital image manipulation and transmission techniques has created a pressing need for the protection of the intellectual property rights on images. A copyright protection method that is based on hiding an ‘invisible’ signal, known as digital watermark, in the image is presented in this paper. Watermark casting is performed in the spatial domain by slightly modifying the intensity of randomly selected image pixels. Watermark detection does not require the existence of the original image and is carried out by comparing the mean intensity value of the marked pixels against that of the pixels not marked. Statistical hypothesis testing is used for this purpose. Pixel modifications can be done in such a way that the watermark is resistant to JPEG compression and lowpass filtering. This is achieved by minimizing the energy content of the watermark signal at higher frequencies while taking into account properties of the human visual system. A variation that generates image dependent watermarks as well as a method to handle geometrical distortions are presented. An extension to color images is also pursued. Experiments on real images verify the effectiveness of the proposed techniques.

Robust audio watermarking in the time domain

The audio watermarking method presented below offers copyright protection to an audio signal by modifying its temporal characteristics. The amount of modification embedded is limited by the necessity that the output signal must not be perceptually different from the original one. The watermarking method presented here does not require the original signal for watermark detection. The watermark key is simply a seed known only by the copyright owner. This seed creates the watermark signal to be embedded. Watermark embedding depends also on the audio signal amplitude in a way that minimizes the audibility of the watermark signal. The embedded watermark is robust to MPEG audio coding, filtering, resampling and requantization.

Video shot detection and condensed representation. a review

There is an urgent need to develop techniques that organize video data into more compact forms or extract semantically meaningful information. Such operations can serve as a first step for a number of different data access tasks such as browsing, retrieval, genre classification, and event detection. In this paper, we focus not on the high-level video analysis task themselves but on the common basic techniques that have been developed to facilitate them. These basic tasks are shot boundary detection and condensed video representation

The i3DPost Multi-View and 3D Human Action/Interaction Database

In this paper a new multi-view/3D human action/interaction database is presented. The database has been created using a convergent eight camera setup to produce high definition multi-view videos, where each video depicts one of eight persons performing one of twelve different human motions. Various types of motions have been recorded, i.e., scenes where one person performs a specific movement, scenes where a person executes different movements in a succession and scenes where two persons interact with each other. Moreover, the subjects have different body sizes, clothing and are of different sex, nationalities, etc.. The multi-view videos have been further processed to produce a 3D mesh at each frame describing the respective 3D human body surface. To increase the applicability of the database, for each person a multi-view video depicting the person performing sequentially the six basic facial expressions separated by the neutral expression has also been recorded. The database is freely available for research purposes.

Digital image watermarking: an overview

Usage of digital media has witnessed a tremendous growth during the last decades, as a result of their notable benefits in efficient storage, ease of manipulation and transmission. However these features make digital media vulnerable to copyright infringement, tampering and unauthorized distribution. In the last five years the protection of digital information has received significant attention within the digital media community, and a number of techniques that try to address the problem by hiding appropriate information (e.g. copyright or authentication data) within digital media have been proposed. In this paper we review data hiding techniques for copyright protection of still images and describe some recent research results in this field.

Digital image processing techniques for the detection and removal of cracks in digitized paintings

An integrated methodology for the detection and removal of cracks on digitized paintings is presented in this paper. The cracks are detected by thresholding the output of the morphological top-hat transform. Afterward, the thin dark brush strokes which have been misidentified as cracks are removed using either a median radial basis function neural network on hue and saturation data or a semi-automatic procedure based on region growing. Finally, crack filling using order statistics filters or controlled anisotropic diffusion is performed. The methodology has been shown to perform very well on digitized paintings suffering from cracks.

3D Human Action Recognition for Multi-view Camera Systems

This paper presents a novel approach for combining optical flow into enhanced 3D motion vector fields for human action recognition. Our approach detects motion of the actors by computing optical flow in video data captured by a multi-view camera setup with an arbitrary number of views. Optical flow is estimated in each view and extended to 3D using 3D reconstructions of the actors and pixel-to-vertex correspondences. The resulting 3D optical flow for each view is combined into a 3D motion vector field by taking the significance of local motion and its reliability into account. 3D Motion Context (3D-MC) and Harmonic Motion Context (HMC) are used to represent the extracted 3D motion vector fields efficiently and in a view-invariant manner, while considering difference in anthropometry of the actors and their movement style variations. The resulting 3D-MC and HMC descriptors are classified into a set of human actions using normalized correlation, taking into account the performing speed variations of different actors. We compare the performance of the 3D-MC and HMC descriptors, and show promising experimental results for the publicly available i3DPost Multi View Human Action Dataset.

Nonnegative Matrix Factorization in Polynomial Feature Space

Plenty of methods have been proposed in order to discover latent variables (features) in data sets. Such approaches include the principal component analysis (PCA), independent component analysis (ICA), factor analysis (FA), etc., to mention only a few. A recently investigated approach to decompose a data set with a given dimensionality into a lower dimensional space is the so-called nonnegative matrix factorization (NMF). Its only requirement is that both decomposition factors are nonnegative. To approximate the original data, the minimization of the NMF objective function is performed in the Euclidean space, where the difference between the original data and the factors can be minimized by employing L 2-norm. In this paper, we propose a generalization of the NMF algorithm by translating the objective function into a Hilbert space (also called feature space) under nonnegativity constraints. With the help of kernel functions, we developed an approach that allows high-order dependencies between the basis images while keeping the nonnegativity constraints on both basis images and coefficients. Two practical applications, namely, facial expression and face recognition, show the potential of the proposed approach.

A benchmarking protocol for watermarking methods

A benchmarking system for watermarking algorithms is described. The proposed benchmarking system can be used to evaluate the performance of watermarking methods used for copyright protection, authentication, fingerprinting, etc. Although the system described is used for image watermarking, the general framework can be used, by introducing a different set of attacks, for benchmarking of video and audio data.

An Optimal Detector Structure for the Fourier Descriptors Domain Watermarking of 2D Vector Graphics

Polygonal lines constitute a key graphical primitive in 2D vector graphics data. Thus, the ability to apply a digital watermark to such an entity would enable the watermarking of cartoons, drawings, and geographical information systems (GIS) data in vector graphics format. This paper builds on and extends an existing algorithm that achieves polygonal line watermarking by modifying the Fourier descriptors magnitude in an imperceptible way. Watermarks embedded by this technique can be detected in rotated, translated, scaled, or reflected polygonal lines. The detection of such watermarks had been previously carried out through a correlator detector. In this paper, analysis of the statistics of the Fourier descriptors is exploited to devise an optimal blind detector. Furthermore, the problem of watermarking multiple lines, as well as other implementation issues are being addressed. Experimental results verify the imperceptibility and robustness of the proposed method.