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Springer Adversarial Machine Learning - AI Security Book

Amazon

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Description

Deep learning networks face significant vulnerabilities from intelligent cyber adversaries. Even small, innocuous perturbations to training data can manipulate deep network behavior in unintended ways. This Springer publication reviews the latest developments in adversarial attack technologies across multiple domains, including computer vision, natural language processing, and cybersecurity. By examining multidimensional, textual, image, sequence, and temporal data, this book provides a comprehensive look at how attacks function. It assesses the robustness properties of deep learning networks to help researchers and professionals understand the current landscape of AI security. This text serves as a vital resource for understanding the defense mechanisms and learning theories required to protect artificial intelligence systems from sophisticated threats.

Key Features

This Springer book examines the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries.

The text reviews recent developments in adversarial attack technologies specifically for computer vision and natural language processing.

Content covers security threats related to multidimensional, textual, image, sequence, and temporal data.

The book assesses the robustness properties of deep learning networks to provide a clear understanding of AI defense mechanisms.

This resource explores the connection between adversarial attacks and the learning theories used in artificial intelligence.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
07 March 2023
Listed Since
01 March 2022

Barcode

No barcode data available

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