Kooperativer Bibliotheksverbund

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Format: 1 online resource (300 pages)

ISBN: 0-12-824257-4

Note: Front Cover -- Adversarial Robustness for Machine Learning -- Copyright -- Contents -- Biography -- Dr. Pin-Yu Chen (1986-present) -- Dr. Cho-Jui Hsieh (1985-present) -- Preface -- Part 1 Preliminaries -- 1 Background and motivation -- 1.1 What is adversarial machine learning? -- 1.2 Mathematical notations -- 1.3 Machine learning basics -- 1.4 Motivating examples -- Adversarial robustness < -- > -- accuracy - what standard accuracy fails to tell -- Fast adaptation of adversarial robustness evaluation assets for emerging machine learning models -- 1.5 Practical examples of AI vulnerabilities -- 1.6 Open-source Python libraries for adversarial robustness -- Part 2 Adversarial attack -- 2 White-box adversarial attacks -- 2.1 Attack procedure and notations -- 2.2 Formulating attack as constrained optimization -- 2.3 Steepest descent, FGSM and PGD attack -- 2.4 Transforming to an unconstrained optimization problem -- 2.5 Another way to define attack objective -- 2.6 Attacks with different lp norms -- 2.7 Universal attack -- 2.8 Adaptive white-box attack -- 2.9 Empirical comparison -- 2.10 Extended reading -- 3 Black-box adversarial attacks -- 3.1 Evasion attack taxonomy -- 3.2 Soft-label black-box attack -- 3.3 Hard-label black-box attack -- 3.4 Transfer attack -- 3.5 Attack dimension reduction -- 3.6 Empirical comparisons -- 3.7 Proof of Theorem 1 -- 3.8 Extended reading -- 4 Physical adversarial attacks -- 4.1 Physical adversarial attack formulation -- 4.2 Examples of physical adversarial attacks -- 4.3 Empirical comparison -- 4.4 Extending reading -- 5 Training-time adversarial attacks -- 5.1 Poisoning attack -- 5.2 Backdoor attack -- 5.3 Empirical comparison -- 5.4 Case study: distributed backdoor attacks on federated learning -- 5.5 Extended reading -- 6 Adversarial attacks beyond image classification -- 6.1 Data modality and task objectives. , 6.2 Audio adversarial example -- 6.3 Feature identification -- 6.4 Graph neural network -- 6.5 Natural language processing -- Sentence classification -- Sequence-to-sequence translation -- 6.6 Deep reinforcement learning -- 6.7 Image captioning -- 6.8 Weight perturbation -- 6.9 Extended reading -- Part 3 Robustness verification -- 7 Overview of neural network verification -- 7.1 Robustness verification versus adversarial attack -- 7.2 Formulations of robustness verification -- 7.3 Applications of neural network verification -- Safety-critical control systems -- Natural language processing -- Machine learning interpretability -- 7.4 Extended reading -- 8 Incomplete neural network verification -- 8.1 A convex relaxation framework -- 8.2 Linear bound propagation methods -- The optimal layerwise convex relaxation -- 8.3 Convex relaxation in the dual space -- 8.4 Recent progresses in linear relaxation-based methods -- 8.5 Extended reading -- 9 Complete neural network verification -- 9.1 Mixed integer programming -- 9.2 Branch and bound -- 9.3 Branch-and-bound with linear bound propagation -- 9.4 Empirical comparison -- 10 Verification against semantic perturbations -- 10.1 Semantic adversarial example -- 10.2 Semantic perturbation layer -- 10.3 Input space refinement for semantify-NN -- 10.4 Empirical comparison -- Part 4 Adversarial defense -- 11 Overview of adversarial defense -- 11.1 Empirical defense versus certified defense -- 11.2 Overview of empirical defenses -- 12 Adversarial training -- 12.1 Formulating adversarial training as bilevel optimization -- 12.2 Faster adversarial training -- 12.3 Improvements on adversarial training -- 12.4 Extended reading -- 13 Randomization-based defense -- 13.1 Earlier attempts and the EoT attack -- 13.2 Adding randomness to each layer -- 13.3 Certified defense with randomized smoothing -- 13.4 Extended reading. , 14 Certified robustness training -- 14.1 A framework for certified robust training -- 14.2 Existing algorithms and their performances -- Interval bound propagation (IBP) -- Linear relaxation-based training -- 14.3 Empirical comparison -- 14.4 Extended reading -- 15 Adversary detection -- 15.1 Detecting adversarial inputs -- 15.2 Detecting adversarial audio inputs -- 15.3 Detecting Trojan models -- 15.4 Extended reading -- 16 Adversarial robustness of beyond neural network models -- 16.1 Evaluating the robustness of K-nearest-neighbor models -- A primal-dual quadratic programming formulation -- Dual quadratic programming problems -- Robustness verification for 1-NN models -- Efficient algorithms for computing 1-NN robustness -- Extending beyond 1-NN -- Robustness of KNN vs neural network on simple problems -- 16.2 Defenses with nearest-neighbor classifiers -- 16.3 Evaluating the robustness of decision tree ensembles -- Robustness of a single decision tree -- Robustness of ensemble decision stumps -- Robustness of ensemble decision trees -- Training robust tree ensembles -- 17 Adversarial robustness in meta-learning and contrastive learning -- 17.1 Fast adversarial robustness adaptation in model-agnostic meta-learning -- When and how to incorporate robust regularization in MAML? -- 17.2 Adversarial robustness preservation for contrastive learning: from pretraining to finetuning -- Part 5 Applications beyond attack and defense -- 18 Model reprogramming -- 18.1 Reprogramming voice models for time series classification -- 18.2 Reprogramming general image models for medical image classification -- 18.3 Theoretical justification of model reprogramming -- 18.4 Proofs -- 18.5 Extended reading -- 19 Contrastive explanations -- 19.1 Contrastive explanations method -- 19.2 Contrastive explanations with monotonic attribute functions -- 19.3 Empirical comparison. , 19.4 Extended reading -- 20 Model watermarking and fingerprinting -- 20.1 Model watermarking -- 20.2 Model fingerprinting -- 20.3 Empirical comparison -- 20.4 Extended reading -- 21 Data augmentation for unsupervised machine learning -- 21.1 Adversarial examples for unsupervised machine learning models -- 21.2 Empirical comparison -- References -- Index -- Back Cover.

Additional Edition: Print version: Chen, Pin-Yu Adversarial Robustness for Machine Learning San Diego : Elsevier Science & Technology,c2022 ISBN 9780128240205

Language: English