YoVDO

Towards Evaluating the Robustness of Neural Networks

Offered By: IEEE via YouTube

Tags

IEEE Symposium on Security and Privacy Courses Cybersecurity Courses Machine Learning Courses Neural Networks Courses

Course Description

Overview

Explore a conference talk that delves into the vulnerability of neural networks to adversarial examples and critically examines the effectiveness of defensive distillation. Learn about three new attack algorithms that successfully generate adversarial examples for both distilled and undistilled neural networks with 100% probability. Discover how these attacks are tailored to different distance metrics and compare their effectiveness to previous adversarial example generation algorithms. Gain insights into the proposed use of high-confidence adversarial examples in a transferability test that can potentially break defensive distillation. Understand the importance of this research in establishing benchmarks for future defense attempts aimed at creating neural networks resistant to adversarial examples.

Syllabus

Towards Evaluating the Robustness of Neural Networks


Taught by

IEEE Symposium on Security and Privacy

Tags

Related Courses

Neural Networks for Machine Learning
University of Toronto via Coursera
Good Brain, Bad Brain: Basics
University of Birmingham via FutureLearn
Statistical Learning with R
Stanford University via edX
Machine Learning 1—Supervised Learning
Brown University via Udacity
Fundamentals of Neuroscience, Part 2: Neurons and Networks
Harvard University via edX