Universal Image Steganalytic Method

In the paper we introduce a new universal steganalytic method in JPEG file format that is detecting well-known and also newly developed steganographic methods. The steganalytic model is trained by MHF-DZ steganographic algorithm previously designed by the same authors. The calibration technique with the Feature Based Steganalysis (FBS) was employed in order to identify statistical changes caused by embedding a secret data into original image. The steganalyzer concept utilizes Support Vector Machine (SVM) classification for training a model that is later used by the same steganalyzer in order to identify between a clean (cover) and steganographic image. The aim of the paper was to analyze the variety in accuracy of detection results (ACR) while detecting testing steganographic algorithms as F5, Outguess, Model Based Steganography without deblocking, JP Hide&Seek which represent the generally used steganographic tools. The comparison of four feature vectors with different lengths FBS (22), FBS (66) FBS(274) and FBS(285) shows promising results of proposed universal steganalytic method comparing to binary methods

A fast distance between histograms

Iberoamerican Congress on Pattern Recognition (CIARP), 2005, Havana (Cuba)In this paper we present a new method for comparing histograms. Its main advantage is that it takes less time than previous methods. The present distances between histograms are defined on a structure called signature, which is a lossless representation of histograms. Moreover, the type of the elements of the sets that the histograms represent are ordinal, nominal and modulo. We show that the computational cost of these distances is O(z′) for the ordinal and nominal types and O(z′2) for the modulo one, where z′ is the number of non-empty bins of the histograms. In the literature, the computational cost of the algorithms presented depends on the number of bins in the histograms. In most applications, the histograms are sparse, so considering only the non-empty bins dramatically reduces the time needed for comparison. The distances we present in this paper are experimentally validated on image retrieval and the positioning of mobile robots through image recognition.Peer Reviewe

Perceptually Motivated Shape Context Which Uses Shape Interiors

In this paper, we identify some of the limitations of current-day shape matching techniques. We provide examples of how contour-based shape matching techniques cannot provide a good match for certain visually similar shapes. To overcome this limitation, we propose a perceptually motivated variant of the well-known shape context descriptor. We identify that the interior properties of the shape play an important role in object recognition and develop a descriptor that captures these interior properties. We show that our method can easily be augmented with any other shape matching algorithm. We also show from our experiments that the use of our descriptor can significantly improve the retrieval rates

Ising Spins on Thin Graphs

The Ising model on ``thin'' graphs (standard Feynman diagrams) displays several interesting properties. For ferromagnetic couplings there is a mean field phase transition at the corresponding Bethe lattice transition point. For antiferromagnetic couplings the replica trick gives some evidence for a spin glass phase. In this paper we investigate both the ferromagnetic and antiferromagnetic models with the aid of simulations. We confirm the Bethe lattice values of the critical points for the ferromagnetic model on $\phi^3$ and $\phi^4$ graphs and examine the putative spin glass phase in the antiferromagnetic model by looking at the overlap between replicas in a quenched ensemble of graphs. We also compare the Ising results with those for higher state Potts models and Ising models on ``fat'' graphs, such as those used in 2D gravity simulations.Comment: LaTeX 13 pages + 9 postscript figures, COLO-HEP-340, LPTHE-Orsay-94-6

EsPRESSo: Efficient Privacy-Preserving Evaluation of Sample Set Similarity

Electronic information is increasingly often shared among entities without complete mutual trust. To address related security and privacy issues, a few cryptographic techniques have emerged that support privacy-preserving information sharing and retrieval. One interesting open problem in this context involves two parties that need to assess the similarity of their datasets, but are reluctant to disclose their actual content. This paper presents an efficient and provably-secure construction supporting the privacy-preserving evaluation of sample set similarity, where similarity is measured as the Jaccard index. We present two protocols: the first securely computes the (Jaccard) similarity of two sets, and the second approximates it, using MinHash techniques, with lower complexities. We show that our novel protocols are attractive in many compelling applications, including document/multimedia similarity, biometric authentication, and genetic tests. In the process, we demonstrate that our constructions are appreciably more efficient than prior work.Comment: A preliminary version of this paper was published in the Proceedings of the 7th ESORICS International Workshop on Digital Privacy Management (DPM 2012). This is the full version, appearing in the Journal of Computer Securit

Unexpected series of regular frequency spacing of delta Scuti stars in the non-asymptotic regime -- I. The methodology

A sequence search method was developed to search regular frequency spacing in delta Scuti stars by visual inspection and algorithmic search. We searched for sequences of quasi-equally spaced frequencies, containing at least four members per sequence, in 90 delta Scuti stars observed by CoRoT. We found an unexpectedly large number of independent series of regular frequency spacing in 77 delta Scuti stars (from 1 to 8 sequences) in the non-asymptotic regime. We introduce the sequence search method presenting the sequences and echelle diagram of CoRoT 102675756 and the structure of the algorithmic search. Four sequences (echelle ridges) were found in the 5-21 d^{-1} region, where the pairs of the sequences are shifted (between 0.5-0.59 d^{-1}) by twice the value of the estimated rotational splitting frequency (0.269 d^{-1}). The general conclusions for the whole sample are also presented in this paper. The statistics of the spacings derived by the sequence search method, by FT and that of the shifts are also compared. In many stars, more than one almost equally valid spacing appeared. The model frequencies of FG Vir and their rotationally split components were used to reveal a possible explanation that one spacing is the large separation, while the other is a sum of the large separation and the rotational frequency. In CoRoT 102675756, the two spacings (2.249 and 1.977 d^{-1}) agree better with the sum of a possible 1.710 d^{-1} large separation and two or one times, respectively, the value of the rotational frequency.Comment: 12 pages, 10 figures, accepted for publication in Ap

From 3D Point Clouds to Pose-Normalised Depth Maps

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)