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401-4661-DRL 2 Credits DR D-MATH

Robustness of Deep Neural Networks

Lecturers & Examiners: Dr. Rima Alaifari
Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to with their name, course number and student ID. Please see
VVZ CR n/a

Last Updated: 2026-02-05 16:02:04

Abstract

While deep neural networks have been very successfully employed in classification problems, their stability properties remain still unclear. In particular, the presence of adversarial examples has demonstrated that state-of-the-art networks are vulnerable to small perturbations in the data. This course serves as an introduction to adversarial attacks and defenses for deep neural nework algorithms.

Objective

1. Theory: in this course, we will discuss the trade-off between accuracy and stability of classification algorithms and study the state-of-the-art for robust image classification, adversarial attacks and adversarial training. 2. Practice: students will train and attack deep neural networks themselves, to get a hands-on experience.

General Information

Language
English
Levels
DR

Examination

Type
ungraded semester performance

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture with exercise Robustness of Deep Neural Networks
  • Thu 14:15-16:00 (RZ F 21)
2 h weekly
independent project Robustness of Deep Neural Networks No time listed 1 h weekly

Offered In