Chen, Pin-Yu, <br/><br/>
Adversarial robustness for machine learning / 
Pin-Yu Chen and Cho-Jui Hsieh. 
 - xiv, 283 pages :  illustrations ;  23 cm 
<br/><br/>Includes bibliographical references and index. 
<br/><br/>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. 
<br/><br/><label>ISBN: </label>9780128240205 
<br/><br/><label>LCCN: </label>2023276771 
<br/><br/><label>Nat. Bib. No.: </label>GBC2C1035  bnb 
<br/><br/><label>Nat. Bib. Agency Control No.: </label>020675475  Uk 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Machine learning.<br/>Robust optimization.<br/>Machine learning.<br/>Robust optimization.
<br/><br/><label>LC Class. No.: </label>Q325.5  / .C44 2023 
<br/><br/><label>Dewey Class. No.: </label>006.31  / C420a