FILLATRE , Lionel

This paper deals with the detection of hidden bits in the Least Significant Bit (LSB) plane of a natural image. The mean level and the covariance matrix of the image, considered as a quantized Gaussian random matrix, are unknown. An adaptive statistical test is designed such that its probability distribution is always independent of the unknown...

International audience

This paper deals with the detection of hidden bits in the Least Significant Bit (LSB) plane of a natural image. The mean level and the covariance matrix of the image, considered as a quantized Gaussian random matrix, are unknown. An adaptive statistical test is designed such that its probability distribution is always independent of the unknown...

This paper develops a multihypothesis testing framework for calculating numerically the optimal minimax test with discrete observations and an arbitrary loss function. Discrete observations are common in data processing and make tractable the calculation of the minimax test. Each hypothesis is both associated to a parameter defining the...

International audience

Cet article s'intéresse au calcul d'un test minimax de niveau contraint entre plusieurs hypothèses impliquant des observations discrètes et une fonction de perte arbitraire. Le test minimax de niveau contraint minimise le risque de classification maximum et il garantit simultanément que la probabilité de rejeter l'hypothèse nulle, appelé le...

International audience

International audience

This paper addresses the problem of multiple hypothesis testing (detection and isolation of mean vectors) in the case of Gaussian linear model with nuisance parameters. An invariant constrained asymptotically uniformly minimax test is proposed to solve this problem. The invariance of the test with respect to the nuisance parameters is obtained...

International audience

International audience

International audience

This paper addresses the problem of multiple hypothesis testing (detection and isolation of mean vectors) in the case of Gaussian linear model with nuisance parameters. An invariant constrained asymptotically uniformly minimax test is proposed to solve this problem. The invariance of the test with respect to the nuisance parameters is obtained...

This paper studies the problem of classifying some Gaussian samples into one of two parametric probabilistic models, also called sources, when the parameter and the a priori probability of each source are unknown. Each source is governed by an univariate normal distribution whose mean is unknown. A training sequence is available for each source...

In this paper, we present the optimization procedure for computing the discrete boxconstrained minimax classifier introduced in [1, 2]. Our approach processes discrete or beforehand discretized features. A box-constrained region defines some bounds for each class proportion independently. The box-constrained minimax classifier is obtained from...

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International audience

International audience

International audience

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The anomaly localization in distributed networks can be treated as a multiple hypothesis testing (MHT) problem and the Bayesian test with 0-1 loss function is a standard solution to this problem. However, For the anomaly localization application, the cost of different false localization varies, which cannot be reflected by the 0 - 1 loss...

This article focuses on approximating an interpretable neural network with kernel logistic regression. We introduce a new kernel that directly stems from the architecture of the neural network. The decision rule resulting from a logistic regression applied to this kernel is modeled as an additive decomposition of univariate functions and is...