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Adversarial Learning

(CTU-AI410.AU1) / ISBN : 979-8-90059-942-7
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1

Fundamentals of Adversarial Learning

  • Adversarial Learning Frameworks
  • Adversarial Security Mechanisms
  • Stochastic Game Illustration in Adversarial Deep Learning
  • Learning Curve Analysis for Supervised Machine Learning
  • Adversarial Loss Functions for Discriminative Learning
  • Adversarial Examples in Deep Networks
  • Adversarial Examples for Misleading Classifiers
2

Applying Adversarial Techniques

  • Generative Adversarial Networks
  • Generative Adversarial Networks for Adversarial Learning
  • Transfer Learning for Domain Adaptation
3

Defense Strategies Against Adversarial Attacks

  • Security and Privacy in Adversarial Learning
  • Feature Weighting Attacks
  • Poisoning Support Vector Machines
  • Robust Classifier Ensembles
  • Robust Clustering Models
  • Robust Feature Selection Models
  • Robust Anomaly Detection Models
  • Robust Task Relationship Models
  • Robust Regression Models
  • Adversarial Machine Learning in Cybersecurity
  • Securing Classifiers Against Feature Attacks
  • Adversarial Classification Tasks with Regularizers
  • Adversarial Reinforcement Learning
  • Computational Optimization Algorithmics for Game Theoretical Adversarial Learning
  • Defense Mechanisms in Adversarial Machine Learning
4

Ethical Implications of Adversarial Learning

  • Game Theoretical Learning Models
  • Game Theoretical Adversarial Learning
  • Game Theoretical Adversarial Deep Learning
  • Stochastic Games in Predictive Modeling
  • Robust Game Theory in Adversarial Learning Games
5

Applying Adversarial Techniques - Advanced Topics

  • Adversarial Attacks on Images
  • Adversarial Attacks on Texts
  • Spam Filtering

1

Fundamentals of Adversarial Learning

  • Exploring the Adversarial Learning Framework
  • Comparing Classifier Robustness Against Adversarial Attacks
  • Evaluating Classifier Performance Under Gaussian Noise
  • Simulating Stochastic Defender-Attacker Decisions
  • Understanding Adversarial Examples
  • Fooling a Neural Network with Tiny Perturbations
2

Applying Adversarial Techniques

  • Building and Training a Simple GAN
  • Understanding a Black-Box Attack
  • Evaluating Transfer Learning Across Different Data Domains
3

Defense Strategies Against Adversarial Attacks

  • Performing a Simple Dataset Poisoning Attack
  • Analyzing Classifier Behavior Under Input Perturbations
  • Modeling Learner-Versus-Adversary Interactions
  • Exploring Adversarial Attack Surfaces
  • Understanding Adversarial Defense Mechanisms
4

Ethical Implications of Adversarial Learning

  • Identifying Suspicious Inputs Using Prediction Confidence
  • Analyzing Game-Theoretical Adversarial Interaction
  • Protecting an IDS Against Adversarial Inputs
  • Protecting an IDS Against Adversarial Inputs
5

Applying Adversarial Techniques - Advanced Topics

  • Generating Adversarial Images to Mislead Classifiers
  • Exploring Character-Level Perturbations in Text Classification
  • Understanding Spam Filtering
  • Evading and Strengthening Spam Filters Against Adversarial Messages

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