Wioletta Wójtowicz

Digital images authentication scheme based on bimodal biometric watermarking in an independent domain

We propose a new biometric-based bimodal watermarking scheme.Two independent watermarks based on fingerprint and iris biometrics are used.Our algorithm explores an independent domain of the cover image.Authentication procedure is based on fusion of two extracted biometric watermarks. With the growing accessibility and usability of internet there is a growing concern over content protection of digital images. Recently, to eliminate the traditional use of passwords and to ensure that the access to the image is restricted only to legitimate users, security solutions are increasingly combined with biometrics. Consequently, biometric-based watermarking algorithms, that involve embedding the identity of the owner, are proposed to solve ownership disputes. This paper presents a new scheme for protecting and authenticating invisibly watermarked digital images. It applies Independent Component Analysis to the cover image and enables the insertion of two independent watermarks based on fingerprint and iris biometrics. In this approach biometric techniques are used for watermarks generation and for owners authentication. The main advantage of proposed algorithm is construction of ICA based watermarking domain to enable insertion of two independent watermarks, that improve authentication accuracy and makes scheme more robust.

PARMA Models with Applications in R

Periodic autoregressive–moving-average models (periodic ARMA models, PARMA models) are used to model non-stationary time series with periodic structure. They are similar to ARMA except the coefficients that are periodic in time with a common period. They are widely applied in climatology, hydrology, meteorology and economics data. In this paper we want to familiarize the readers with all the essential steps of PARMA model fitting. We present in detail the non-parametric spectral analysis, model identification, parameter estimation, diagnostic checking (model verification) and prediction on the real data example. Our aim is to provide appropriate tool for the complete analysis of periodic time series using PARMA modelling and to popularize this approach among non-specialists.

Biometric watermarks based on face recognition methods for authentication of digital images

Because making digital images secure runs into the substantial challenge of owner authentication, many security schemes based on cryptography, steganography and watermarking technology include biometric recognition methods. To follow on these studies, this paper describes a combination of facial images with watermarking technology to automatically authenticate digital images owners/users. In the proposed methodology, biometric face recognition methods such as principal component analysis and eigenfeature regularization and extraction produce vectorial representations of facial images. These vectors are used as copyright watermarks, in a few common watermarking schemes, and are tested for identification purposes after they are extracted. Initially, watermarking algorithms are studied with some arbitrary cover image, and also the most robust algorithm is tested for different cover images of particular subjects. The strength of this paper is finding relationships between the original and extracted biometric data using neural networks instead of the most common, simple measures such as correlation coefficients or distance metrics. The NN subject identification is performed directly, as there is no need to reconstruct facial images after the watermarks are extracted, compute templates for particular subjects, or seek a suitable distance metric. What is more, the presented study includes a performance comparison of two machine learning methods, frequently used for face recognition, and of a few popular watermarking algorithms. Very promising identification results were obtained in many considered experiments, even those involving attacks on watermarked images. Copyright

Bootstrap for Maximum Likelihood Estimates of PARMA Coefficients

In this chapter we use bootstrap techniques to estimate empirical distributions of parameter estimates for PAR sequences determined by maximum likelihood techniques. The parameters are not the periodic autoregression parameters, but are the coefficients in the Fourier series representing the parameters. We compare two different bootstrap techniques, IID and GSBB, applied to the residuals of the maximum likelihood estimation. The IID method seems a little better, which is not a surprise since the conditions for the GSBB are not completely satisfied. We expect these method to also work satisfactorily for full PARMA estimations, where both PMA and PAR terms are present in the model.

An introduction to watermarking of medical images

Abstract This paper provides a preliminary investigation on digital watermarking as an effective technology to protect property rights and limit distribution of multimedia data. First, crucial properties and design requirements of watermarking schemes are discussed. Then, as watermarking techniques finds many applications in healthcare industry, aspects of medical image watermarking are raised. Nowadays, the transmission of digitized medical information has become very easy due to the generality of Internet. However, the digital form of these images can easily be manipulated and degraded. This causes problems of medical security and copyright protection and poses a great challenge to privacy protection using watermarking techniques.