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Intro

Overview

Paper History and Authors

Motivation

Adversarial Examples for Linear Models

Adversarial Example for Non-Linear Models • Is it applicable for nonlinear models?

Summarizing FGSM

Experimental Results ► GSM band attack on Neural network with different activation function

Adversarial Training (AT)

FGSM Attack to a Logistic Regression Model

Adversarial Training for Logistic Regression Model

L1 regularization for Logistic Regression Model • To prevent the overfitting problem

Adversarial Training vs L1 weight decay • Training maxout networks on MNIST . Good results using adversarial training with = 0.25

Adversarial Training of DNN

Adversarial Trained Model

Other Considerations

Why Do Adversarial Examples Generalize?

Generalization of Adversarial Examples

Alternative Hypothesis

Strengths

Weaknesses

Summary

Description:

Explore the concept of adversarial examples in machine learning through this 27-minute lecture from the University of Central Florida. Delve into the history, motivation, and application of adversarial examples for both linear and non-linear models. Examine the Fast Gradient Sign Method (FGSM) and its experimental results on neural networks with different activation functions. Learn about adversarial training techniques for logistic regression and deep neural networks, comparing their effectiveness to L1 regularization. Investigate the generalization of adversarial examples across different models and consider alternative hypotheses. Analyze the strengths and weaknesses of adversarial training approaches, gaining a comprehensive understanding of this important topic in machine learning security and robustness.

Explaining and Harnessing Adversarial Examples in Machine Learning - Spring 2021

University of Central Florida

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#Computer Science #Machine Learning #Machine Learning Security #Artificial Intelligence #Neural Networks #Logistic Regression #Deep Learning #Deep Neural Networks

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