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\title{Fast Reliability Estimation for Neural Networks
with Adversarial Attack-Driven Importance Sampling}

\begin{document}


% The standard author block has changed for UAI 2024 to provide
% more space for long author lists and allow for complex affiliations
%
% All author information is automatically removed by the class for the
% anonymous submission version of your paper, so you can already add your
% information below.
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% Add authors
\author[1,2]{\href{mailto:<titkarim@gmail.com>?Subject=Your UAI 2024 paper}{Karim Tit}}
\author[2]{Teddy Furon}
% Add affiliations after the authors
\affil[1]{%
    University of Luxembourg\\ 
    Luxembourg, LU\footnote{No}
}
\affil[2]{%
    Inria, CNRS, IRISA\\
    University of Rennes\\
    Rennes, FR 

}




\maketitle



\begin{abstract}
  This paper introduces a novel approach to evaluate the reliability of Neural Networks (NNs) by integrating adversarial attacks with Importance Sampling (IS), enhancing the assessment's precision and efficiency. Leveraging adversarial attacks to guide IS, our method efficiently identifies vulnerable input regions, offering a more directed alternative to traditional Monte Carlo methods. While comparing our approach with classical reliability techniques like FORM and SORM, and with classical rare event simulation methods such as Cross-Entropy IS, we acknowledge its reliance on the effectiveness of adversarial attacks and its inability to handle very high-dimensional data such as ImageNet. Despite these challenges, our comprehensive empirical validations on the datasets the MNIST and CIFAR10 demonstrate the method's capability to accurately estimate NN reliability for a variety of models. Our research not only presents an innovative strategy for reliability assessment in NNs but also sets the stage for further work exploiting the connection between adversarial robustness and the field of statistical reliability engineering.
\end{abstract}
\setcounter{footnote}{1}
\footnotetext{The majority of this work was carried out while Karim Tit was a Ph.D. candidate at the University of Rennes.}
\input{notations}
\input{introduction}
\input{problem}
\input{background}
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\input{method}
\input{experiments}


\input{conclusion}



\begin{acknowledgements} % will be removed in pdf for initial submission,
						 % (without ‘accepted’ option in \documentclass)
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    We thank French ANR and AID agencies for funding Chaire SAIDA ANR-20-CHIA-0011-01. 

\end{acknowledgements}

% References
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\title{Supplementary Material}
\maketitle



\appendix
\input{additional}

\end{document}
