CS 502 Directed Studies: Adversarial Machine Learning
CS 502 Directed Studies: Adversarial
Machine Learning
Dr. Alex Vakanski
Lecture 1
CS 502, Fall 2020
Introduction to Adversarial Machine Learning
2
Lecture Outline
CS 502, Fall 2020
? Machine Learning (ML) ? Adversarial ML (AML)
? Adversarial examples
? Attack taxonomy ? Common adversarial attacks
? Noise, semantic attack, FGSM, BIM, PGD, DeepFool, CW attack
? Defense against adversarial attacks
? Adversarial training, random resizing and padding, detect adversarial examples
? Conclusion ? References ? Other AML resources
3
Machine Learning (ML)
CS 502, Fall 2020
? ML tasks
? Supervised, unsupervised, semi-supervised, self-supervised, meta learning, reinforcement learning
? Data collection and preprocessing
? Sensors, cameras, I/O devices, etc.
? Apply a ML algorithm
? Training phase: learn ML model (parameter learning, hyperparameter tuning)
? Testing phase (inference): predict on unseen data
Slide credit: Binghui Wang: Adversarial Machine Learning -- An Introduction
4
ML is Ubiquitous
CS 502, Fall 2020
Healthcare
Picture from: He Xiaoyi ? Adversarial Machine Learning
5
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