Identifiant pérenne de la notice : 27548453X
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Note publique d'information : Machine learning algorithms allow computers to learn without being explicitly programmed.
Their application is now spreading to highly sophisticated tasks across multiple domains,
such as medical diagnostics or fully autonomous vehicles. While this development holds
great potential, it also raises new safety concerns, as machine learning has many
specificities that make its behaviour prediction and assessment very different from
that for explicitly programmed software systems. This book addresses the main safety
concerns with regard to machine learning, including its susceptibility to environmental
noise and adversarial attacks. Such vulnerabilities have become a major roadblock
to the deployment of machine learning in safety-critical applications. The book presents
up-to-date techniques for adversarial attacks, which are used to assess the vulnerabilities
of machine learning models; formal verification, which is used to determine if a trained
machine learning model is free of vulnerabilities; and adversarial training, which
is used to enhance the training process and reduce vulnerabilities. The book aims
to improve readers' awareness of the potential safety issues regarding machine learning
models. In addition, it includes up-to-date techniques for dealing with these issues,
equipping readers with not only technical knowledge but also hands-on practical skills
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