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Adversarial robustness for machine learning / Pin-Yu Chen and Cho-Jui Hsieh.

By: Contributor(s): Material type: TextTextPublisher: London, United Kingdom ; San Diego, California : Academic Press, an imprint of Elsevier, 2023Description: xiv, 283 pages : illustrations ; 23 cmContent type:
  • text
  • still image
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780128240205
Subject(s): DDC classification:
  • 006.31 C420a 23
LOC classification:
  • Q325.5 .C44 2023
Contents:
Part 1. Preliminaries -- Background and motivation -- Part 2. Adversarial attack -- White-box adversarial attacks -- Black-box adversarial attacks -- Physical adversarial attacks -- Training-time adversarial attacks -- Adversarial attacks beyond image classification -- Part 3. Robustness verification -- Overview of neural network verification -- Incomplete neural network verification -- Complete neural network verification -- Verification against semantic perturbations -- Part 4. Adversarial defense -- Overview of adversarial defense -- Adversarial training -- Randomization-based defense -- Certified robustness training -- Adversary detection -- Adversarial robustness of beyond neural network models -- Adversarial robustness in meta-learning and contrastive learning -- Part 5. Applications beyond attack and defense -- Model reprogramming -- Contrastive explanations -- Model watermarking and fingerprinting -- Data augmentation for unsupervised machine learning.
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Holdings
Item type Current library Shelving location Call number Copy number Status Date due Barcode
Books Books Main Library Graduate School Library GRD 006.31 C420a 2023 (Browse shelf(Opens below)) 1-2 Available 030257
Books Books Main Library Graduate School Library GRD 006.31 C420a 2023 (Browse shelf(Opens below)) 2-2 Available 030258

Includes bibliographical references and index.

Part 1. Preliminaries -- Background and motivation -- Part 2. Adversarial attack -- White-box adversarial attacks -- Black-box adversarial attacks -- Physical adversarial attacks -- Training-time adversarial attacks -- Adversarial attacks beyond image classification -- Part 3. Robustness verification -- Overview of neural network verification -- Incomplete neural network verification -- Complete neural network verification -- Verification against semantic perturbations -- Part 4. Adversarial defense -- Overview of adversarial defense -- Adversarial training -- Randomization-based defense -- Certified robustness training -- Adversary detection -- Adversarial robustness of beyond neural network models -- Adversarial robustness in meta-learning and contrastive learning -- Part 5. Applications beyond attack and defense -- Model reprogramming -- Contrastive explanations -- Model watermarking and fingerprinting -- Data augmentation for unsupervised machine learning.

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