Steganography: a class of secure and robust algorithms

This research work presents a new class of non-blind information hiding algorithms that are stego-secure and robust. They are based on some finite domains iterations having the Devaney's topological chaos property. Thanks to a complete formalization of the approach we prove security against watermark-only attacks of a large class of steganographic algorithms. Finally a complete study of robustness is given in frequency DWT and DCT domains.Comment: Published in The Computer Journal special issue about steganograph

Efficient Generation of Parallel Spin-images Using Dynamic Loop Scheduling

High performance computing (HPC) systems underwent a significant increase in their processing capabilities. Modern HPC systems combine large numbers of homogeneous and heterogeneous computing resources. Scalability is, therefore, an essential aspect of scientific applications to efficiently exploit the massive parallelism of modern HPC systems. This work introduces an efficient version of the parallel spin-image algorithm (PSIA), called EPSIA. The PSIA is a parallel version of the spin-image algorithm (SIA). The (P)SIA is used in various domains, such as 3D object recognition, categorization, and 3D face recognition. EPSIA refers to the extended version of the PSIA that integrates various well-known dynamic loop scheduling (DLS) techniques. The present work: (1) Proposes EPSIA, a novel flexible version of PSIA; (2) Showcases the benefits of applying DLS techniques for optimizing the performance of the PSIA; (3) Assesses the performance of the proposed EPSIA by conducting several scalability experiments. The performance results are promising and show that using well-known DLS techniques, the performance of the EPSIA outperforms the performance of the PSIA by a factor of 1.2 and 2 for homogeneous and heterogeneous computing resources, respectively

Fast Mojette Transform for Discrete Tomography

A new algorithm for reconstructing a two dimensional object from a set of one dimensional projected views is presented that is both computationally exact and experimentally practical. The algorithm has a computational complexity of O(n log2 n) with n = N^2 for an NxN image, is robust in the presence of noise and produces no artefacts in the reconstruction process, as is the case with conventional tomographic methods. The reconstruction process is approximation free because the object is assumed to be discrete and utilizes fully discrete Radon transforms. Noise in the projection data can be suppressed further by introducing redundancy in the reconstruction. The number of projections required for exact reconstruction and the response to noise can be controlled without comprising the digital nature of the algorithm. The digital projections are those of the Mojette Transform, a form of discrete linogram. A simple analytical mapping is developed that compacts these projections exactly into symmetric periodic slices within the Discrete Fourier Transform. A new digital angle set is constructed that allows the periodic slices to completely fill all of the objects Discrete Fourier space. Techniques are proposed to acquire these digital projections experimentally to enable fast and robust two dimensional reconstructions.Comment: 22 pages, 13 figures, Submitted to Elsevier Signal Processin