Olivier Strauss

A reversible data hiding method for encrypted images

Since several years, the protection of multimedia data is becoming very important. The protection of this multimedia data can be done with encryption or data hiding algorithms. To decrease the transmission time, the data compression is necessary. Since few years, a new problem is trying to combine in a single step, compression, encryption and data hiding. So far, few solutions have been proposed to combine image encryption and compression for example. Nowadays, a new challenge consists to embed data in encrypted images. Since the entropy of encrypted image is maximal, the embedding step, considered like noise, is not possible by using standard data hiding algorithms. A new idea is to apply reversible data hiding algorithms on encrypted images by wishing to remove the embedded data before the image decryption. Recent reversible data hiding methods have been proposed with high capacity, but these methods are not applicable on encrypted images. In this paper we propose an analysis of the local standard deviation of the marked encrypted images in order to remove the embedded data during the decryption step. We have applied our method on various images, and we show and analyze the obtained results.

Information Processing and Management of Uncertainty in Knowledge-Based Systems

These three volumes (CCIS 442, 443, 444) constitute the proceedings of the 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2014, held in Montpellier, France, July 15-19, 2014. The 180 revised full papers presented together with five invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on uncertainty and imprecision on the web of data; decision support and uncertainty management in agri-environment; fuzzy implications; clustering; fuzzy measures and integrals; non-classical logics; data analysis; real-world applications; aggregation; probabilistic networks; recommendation systems and social networks; fuzzy systems; fuzzy logic in boolean framework; management of uncertainty in social networks; from different to same, from imitation to analogy; soft computing and sensory analysis; database systems; fuzzy set theory; measurement and sensory information; aggregation; formal methods for vagueness and uncertainty in a many-valued realm; graduality; preferences; uncertainty management in machine learning; philosophy and history of soft computing; soft computing and sensory analysis; similarity analysis; fuzzy logic, formal concept analysis and rough set; intelligent databases and information systems; theory of evidence; aggregation functions; big data - the role of fuzzy methods; imprecise probabilities: from foundations to applications; multinomial logistic regression on Markov chains for crop rotation modelling; intelligent measurement and control for nonlinear systems.

Variable structuring element based fuzzy morphological operations for single viewpoint omnidirectional images

Morphological tools can provide transformations suitable for real projective images, but the camera and objects to be analyzed have to be positioned in such a manner that a regular mesh on the objects projects a regular mesh on the image. A morphological modification of the image is thus the projection of an equivalent operation on the object. Otherwise, due to perspective effects, a morphological operation on the image is not the projection of an equivalent operation on the objects to be analyzed. With catadioptric omnidirectional images, it is almost impossible to place the sensor such that a regular mesh on the scene projects a regular mesh on the image. Nevertheless, with proper calibration of a central catadioptric system, the projection of a regular structuring element in a scene can be determined for each point on the image. The aim of this paper is to present new morphological operators that use this projective property. These operators make use of a structuring element of varying shape. Since this varying shape cannot be represented as a binary union of pixels, we propose to use a fuzzy extension of the classical gray-level morphology to account for this phenomenon. This fuzzy extension is performed via fuzzy integrals.

Use the Fuzzy Hough transform towards reduction of the precision/uncertainty duality

The Hough transform is a popular method for detecting complex forms in digital images. However, the technique is not very robust since several parameters that determine the scope of the detection results, such as quantization thresholds and intervals, must first be defined. In the present paper, we propose to enhance shape detection with the Hough transform through fuzzy analysis. One chief drawback of the Hough transform, i.e., the uncertainty/precision duality, is thus reduced.

Fuzzy edge detection for omnidirectional images

The use of omnidirectional vision has increased during these past years. It provides a very large field of view. Nevertheless, omnidirectional images contain significant radial distortions and conventional image processing is not adapted to these specific images. This paper presents an edge detector adapted to the image geometry. Fuzzy sets will be used to take into account all imprecisions introduced by the sampling process. The Prewitt filter applied to omnidirectional image will be studied to illustrate this paper.

On the granularity of summative kernels

In this paper, we propose granularity as a new index to characterize the non-specificity of a summative kernel. This index is intended to reflect the behavior of a kernel in the usual signal processing applications. We show, in different experiments, that two kernels having the same granularity have very similar behavior. This granularity-based adaptation is compared to other adaptation methods. These experiments highlight the ability of the granularity index to measure the spreading and collecting properties of a summative kernel.

Rough histograms for robust statistics

Applied statistics are widely used in pattern recognition and other computing applications to find the most likely value of a parameter. The use of classical empirical statistics is based upon assumption about normality of underlying density distribution of data. When the data is corrupted by contaminated noise, then classical tools are usually not robust enough and the estimation of the mode is biased. In this article, we propose to estimate the main mode of a distribution by means of a rough histogram and we show that this estimation is robust to contamination.

Fuzzy Histograms And Density Estimation

The probability density function is a fundamental concept in statistics. Specifying the density function f of a random variable X on Ω gives a natural description of the distribution of X on the universe Ω. When it cannot be specified, an estimate of this density may be performed by using a sample of n observations independent and identically distributed (X1, ..., Xn) of X . Histogram is the oldest and most widely used density estimator for presentation and exploration of observed univariate data. The construction of a histogram consists in partitioning a given reference interval Ω into p bins Ak and in counting the number Acck of observations belonging to each cell Ak. If all the Ak have the same width h, the histogram is said to be uniform or regular. Let 1lAk be the characteristic function of Ak, we have

Spatial data fusion for qualitative estimation of fuzzy request zones: Application on precision viticulture

This article deals with a method used to describe and manage spatial knowledge. Each spatial datum is considered as an information element, whose location and value are independently described. The method proposed uses the information elements to infer the value of any request zone. Data can be either qualitative or quantitative. Our approach is based on a fuzzy granulation of available items of information. The fusion of information elements is performed by an aggregation operator based on the Choquet integral, which analyzes the spatial relevancy of the information elements on the request zone. We also describe an application of the method to a precision viticulture data set.

Generalizing the Wilcoxon rank-sum test for interval data

Here we propose an adaption of Wilcoxons two-sample rank-sum test to interval data. This adaption is interval-valued: it computes the minimum and maximum values of the statistic when we rank the set of all feasible samples (all joint samples compatible with the initial set-valued information). We prove that these bounds can be explicitly computed using a very low computational cost algorithm. Interpreting this generalized test is straightforward: if the obtained interval-valued p-value is on one side of the significance level, we will be able to make a decision (reject/no reject). Otherwise, we will conclude that our information is too vague to lead to a clear decision.Our method is also applicable to quantized data: in the presence of quantized information, the joint sample may contain a high proportion of draws, which can prevent the test from drawing a clear conclusion. According to the usual convention, when there are ties, the ranks for the observations in a tie are taken to be the average of the ranks for those observations. This convention can lead to wrong conclusions. Here, we consider the family of all possible rank permutations, such that a sample containing ties will not just be associated with a single value, but rather with a collection of values for the Wilcoxons rank-sum statistic, with each one of them being associated with a different p-value. When the impact of quantization is too high to lead to a clear decision, our test provides an interval-valued p-value that includes the chosen significance level. It indicates that there is no clear conclusion according to this test.Two different experiments exemplify the properties of the generalized test: the first one illustrates its ability to avoid wrong decisions in the presence of quantized data. The second one shows the performance of the generalized test when used with interval data. We propose an adaption of Wilcoxons two-sample rank-sum test to interval data.Our method is also applicable to quantized data.It leads to interval-valued p-values that are computed at a very low computational cost.Interpretation of this test is straightforward.