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Adversarial Learning

(CTU-AI410.AU1)
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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
  • Adversarial Attacks on Images
  • Adversarial Attacks on Texts
  • Spam Filtering
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

  • Adversarial Perturbation for Privacy Preservation
5

Governance, Policy, and Compliance in Adversarial AI

1

Fundamentals of Adversarial Learning

  • Exploring the Adversarial Learning Framework
  • Understanding Adversarial Examples
2

Applying Adversarial Techniques

  • Understanding a Black-Box Attack
  • Understanding Spam Filtering
3

Defense Strategies Against Adversarial Attacks

  • Exploring Adversarial Attack Surfaces
  • Understanding Adversarial Defense Mechanisms
4

Ethical Implications of Adversarial Learning

  • Understanding Adversarial Perturbation

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Adversarial Learning

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