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Robust AI: Security and Privacy Issues in Machine Learning

Name: Robust AI: Security and Privacy Issues in Machine Learning
Brand: Springer, Berlin
SKU: 9789819563616
Price: 5301 CZK
Availability: InStock

Pre-adoption Scrutiny of Security and Privacy Guarantees of AI Algorithms

Autor: Nandish Chattopadhyay, Anupam Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif, Muhammad Shafique

Jazyk:

Vazba: Pevná

Vydavatel: Springer, Berlin

Dostupnost: 50 % šance

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This book studies in detail the robustness of machine learning (ML) algorithms involved in dealing w...

Informace o knize

Autor

Nandish Chattopadhyay, Anupam Chattopadhyay, Amira Guesmi, Muhammad Abdullah Hanif, Muhammad Shafique

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2026

EAN

9789819563616

Enbook ID

50126861

Vydavatel

Springer, Berlin

Rozměry

155 x 235

Kompletní popis

This book studies in detail the robustness of machine learning (ML) algorithms involved in dealing with vulnerabilities where the errors or malfunctions are both intentional and malicious, therefore being associated with a specific attack model. Reliability is key to the wider adoption of machine learning algorithms in driving regular tasks. There needs to be guaranteed on the success of ML-driven decision-making systems, without errors. It is often seen that an otherwise typically high-performance neural network trained for a specific task, fails under certain circumstances. These vulnerabilities are a key deterrent to reliability and must be addressed before the ubiquitous adoption of AI.

From the machine learning standpoint, this book looks at both critical ingredients, that is the model (neural architecture and its properties) and the training data and from the perspective of Robust AI, the investigation pertains to both Security and Privacy issues. To elaborate on the nomenclature, the Security aspects involve attacks that concern the disruption of the intended machine learning task itself. The Privacy aspect deals with attacks that pertain to leaking sensitive information or IP. A combination of both is necessary to have robust algorithms that can withstand malicious adversaries. The ideas are well described with respect to the available literature and the propositions are studied extensively with many different use cases, on multiple neural architectures and datasets. The content of this book caters to researchers, programmers, engineering, and policymakers who are interested in the implementation of Robust AI and its security and privacy issues in machine learning.

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Robust AI: Security and Privacy Issues in Machine Learning

Pre-adoption Scrutiny of Security and Privacy Guarantees of AI Algorithms

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