lecture 21 adversarial networks
play

Lecture 21: Adversarial Networks CS109B Data Science 2 Pavlos - PowerPoint PPT Presentation

Aug 12, 2023 •139 likes •417 views

Lecture 21: Adversarial Networks CS109B Data Science 2 Pavlos Protopapas and Mark Glickman 1 How vulnerable are Neural Networks? Uses of Neural Networks CS109B, P ROTOPAPAS , G LICKMAN How vulnerable are Neural Networks? CS109B, P ROTOPAPAS ,

  1. Lecture 21: Adversarial Networks CS109B Data Science 2 Pavlos Protopapas and Mark Glickman 1

  2. How vulnerable are Neural Networks? Uses of Neural Networks CS109B, P ROTOPAPAS , G LICKMAN

  3. How vulnerable are Neural Networks? CS109B, P ROTOPAPAS , G LICKMAN

  4. Explaining Adversarial Examples [Goodfellow et. al ‘15] 1. Robust attacks with FGSM 2. Robust defense with Adversarial Training CS109B, P ROTOPAPAS , G LICKMAN

  5. Explaining Adversarial Examples CS109B, P ROTOPAPAS , G LICKMAN

  6. Some of these adversarial examples can even fool humans: CS109B, P ROTOPAPAS , G LICKMAN

  7. Attacking with F ast G radient S ign M ethod (FGSM) W X L x + λ · sign( r x L) ) x ∗ CS109B, P ROTOPAPAS , G LICKMAN

  8. Attacking with F ast G radient S ign M ethod (FGSM) W X L x + λ · sign( r x L) ) x ∗ CS109B, P ROTOPAPAS , G LICKMAN

  9. x + λ · sign( r x L) ) x ∗ CS109B, P ROTOPAPAS , G LICKMAN

  10. Defending with Adversarial Training 1. Generate adversarial examples 2. Adjust labels CS109B, P ROTOPAPAS , G LICKMAN

  11. Defending with Adversarial Training “ Panda ” 1. Generate adversarial examples 2. Adjust labels CS109B, P ROTOPAPAS , G LICKMAN

  12. Defending with Adversarial Training “ Panda ” 1. Generate adversarial examples 2. Adjust labels 3. Add them to the training set 4. Train new network CS109B, P ROTOPAPAS , G LICKMAN

  13. Attack methods post GoodFellow 2015 ● FGSM [Goodfellow et. al ‘15] ● JSMA [Papernot et. al ‘16] ● C&W [Carlini + Wagner ‘16] ● Step-LL [Kurakin et. al ‘17] ● I-FGSM [Tramer et. al ‘18] CS109B, P ROTOPAPAS , G LICKMAN

  14. White box attacks W L x + λ · sign( r x L) ) x ∗ x + λ · r x L ) x ∗ CS109B, P ROTOPAPAS , G LICKMAN

  15. “ Black Box ” Attacks “ Black Box ” Attacks [Papernot et. al ‘17] CS109B, P ROTOPAPAS , G LICKMAN

  16. “ Black Box ” Attacks Examine inputs and outputs of the model CS109B, P ROTOPAPAS , G LICKMAN

  17. “ Black Box ” Attacks Panda CS109B, P ROTOPAPAS , G LICKMAN

  18. “ Black Box ” Attacks Panda Gibbon CS109B, P ROTOPAPAS , G LICKMAN

  19. “ Black Box ” Attacks Panda Gibbon Ostrich CS109B, P ROTOPAPAS , G LICKMAN

  20. “ Black Box ” Attacks Train a model that performs the same as the black box CS109B, P ROTOPAPAS , G LICKMAN

  21. “ Black Box ” Attacks Train a model that performs the same as the black box Panda Gibbon Ostrich CS109B, P ROTOPAPAS , G LICKMAN

  22. “ Black Box ” Attacks Now attack the model you just trained with “ white ” box attack W L x + λ · r x L ) x ∗ x + λ · sign( r x L) ) x ∗ CS109B, P ROTOPAPAS , G LICKMAN

  23. “ Black Box ” Attacks Use those adversarial examples to the “ black ” box CS109B, P ROTOPAPAS , G LICKMAN

  24. CleverHans A Python library to benchmark machine learning systems' vulnerability to adversarial examples. https://github.com/tensorflow/cleverhans http://www.cleverhans.io/ CS109B, P ROTOPAPAS , G LICKMAN

  25. More Defenses Mixup: Smooth decision boundaries: • Mix two training examples • Regularize the derivatives wrt to x Augment training set • x = λ x i + (1 − λ ) x j ˜ y = λ y i + (1 − λ ) y j ˜ P AVLOS P ROTOPAPAS

  26. Physical attacks Object Detection • • Adversarial Stickers CS109B, P ROTOPAPAS , G LICKMAN

  27. Thank you. CS109B, P ROTOPAPAS , G LICKMAN

Recommend

CSC321 Lecture 19: Generative Adversarial Networks Roger Grosse Roger Grosse CSC321 Lecture 19:

CSC321 Lecture 19: Generative Adversarial Networks Roger Grosse Roger Grosse CSC321 Lecture 19:

CSC321 Lecture 19: Generative Adversarial Networks Roger Grosse Roger Grosse CSC321 Lecture 19: Generative Adversarial Networks 1 / 25 Overview In generative modeling, wed like to train a network that models a distribution, such as a

920 views • 25 slides
Adversarial Training Attacks on Deep Networks and Generative Adversarial Networks Erkut Erdem

Adversarial Training Attacks on Deep Networks and Generative Adversarial Networks Erkut Erdem

images from Geris Game (Pixar, 1997) Adversarial Training Attacks on Deep Networks and Generative Adversarial Networks Erkut Erdem Aykut Erdem Levent Karacan Computer Vision Lab, Hacettepe University Outline Part 1: Attacks on

1.11k views • 72 slides
Machine Learning Lecture 13: Generative Adversarial Networks (I) Nevin L. Zhang

Machine Learning Lecture 13: Generative Adversarial Networks (I) Nevin L. Zhang

Machine Learning Lecture 13: Generative Adversarial Networks (I) Nevin L. Zhang lzhang@cse.ust.hk Department of Computer Science and Engineering The Hong Kong University of Science and Technology This set of notes is based on internet

826 views • 54 slides
Adversarial Examples and Adversarial Training Ian Goodfellow, OpenAI Research Scientist Guest

Adversarial Examples and Adversarial Training Ian Goodfellow, OpenAI Research Scientist Guest

Adversarial Examples and Adversarial Training Ian Goodfellow, OpenAI Research Scientist Guest lecture for CS 294-131, UC Berkeley, 2016-10-05 In this presentation Intriguing Properties of Neural Networks Szegedy et al, 2013

660 views • 44 slides
Generative Adversarial Networks, Wasserstein Distance, and Adversarial Loss Zhiyu Min Alibaba

Generative Adversarial Networks, Wasserstein Distance, and Adversarial Loss Zhiyu Min Alibaba

Generative Adversarial Networks, Wasserstein Distance, and Adversarial Loss Zhiyu Min Alibaba AliMe X-Lab Outline GAN Definition and formulation Saddle point optimization Vanishing gradient Alternative objective for Generator

516 views • 25 slides
Advanced Machine Learning CS 7140 - Spring 2018 Lecture 20: Generative Adversarial Networks

Advanced Machine Learning CS 7140 - Spring 2018 Lecture 20: Generative Adversarial Networks

Advanced Machine Learning CS 7140 - Spring 2018 Lecture 20: Generative Adversarial Networks Jan-Willem van de Meent Slide credits: Ian Goodfellow Variational Autoencoders Input Hidden Mean Encoding Hidden Reconstructed Images

698 views • 33 slides
Advanced Machine Learning CS 7140 - Spring 2019 Lecture 20: Generative Adversarial Networks

Advanced Machine Learning CS 7140 - Spring 2019 Lecture 20: Generative Adversarial Networks

Advanced Machine Learning CS 7140 - Spring 2019 Lecture 20: Generative Adversarial Networks Jan-Willem van de Meent Slide credits: Ian Goodfellow Variational Autoencoders Input Hidden Mean Encoding Hidden Reconstructed Images

1.11k views • 38 slides
CS7015 (Deep Learning) : Lecture 23 Generative Adversarial Networks (GANs) Mitesh M. Khapra

CS7015 (Deep Learning) : Lecture 23 Generative Adversarial Networks (GANs) Mitesh M. Khapra

CS7015 (Deep Learning) : Lecture 23 Generative Adversarial Networks (GANs) Mitesh M. Khapra Department of Computer Science and Engineering Indian Institute of Technology Madras 1/38 Mitesh M. Khapra CS7015 (Deep Learning) : Lecture 23 Module

1.8k views • 153 slides
CS7015 (Deep Learning) : Lecture 23 Generative Adversarial Networks (GANs) Mitesh M. Khapra

CS7015 (Deep Learning) : Lecture 23 Generative Adversarial Networks (GANs) Mitesh M. Khapra

CS7015 (Deep Learning) : Lecture 23 Generative Adversarial Networks (GANs) Mitesh M. Khapra Department of Computer Science and Engineering Indian Institute of Technology Madras 1/38 Mitesh M. Khapra CS7015 (Deep Learning) : Lecture 23 Module

499 views • 38 slides
Generative Adversarial Networks Stefano Ermon, Aditya Grover Stanford University Lecture 10

Generative Adversarial Networks Stefano Ermon, Aditya Grover Stanford University Lecture 10

Generative Adversarial Networks Stefano Ermon, Aditya Grover Stanford University Lecture 10 Stefano Ermon, Aditya Grover (AI Lab) Deep Generative Models Lecture 10 1 / 17 Selected GANs https://github.com/hindupuravinash/the-gan-zoo The GAN

590 views • 17 slides
Neglected topics CS 446 Adversarial examples and deep networks 1 / 23 Adversarial

Neglected topics CS 446 Adversarial examples and deep networks 1 / 23 Adversarial

Neglected topics CS 446 Adversarial examples and deep networks 1 / 23 Adversarial examples? Standard ML setup: We have training data; try to do well on withheld testing data. Adversarial/robust ML setup: We have training

672 views • 27 slides
CSC321 Lecture 22: Adversarial Learning Roger Grosse Roger Grosse CSC321 Lecture 22: Adversarial

CSC321 Lecture 22: Adversarial Learning Roger Grosse Roger Grosse CSC321 Lecture 22: Adversarial

CSC321 Lecture 22: Adversarial Learning Roger Grosse Roger Grosse CSC321 Lecture 22: Adversarial Learning 1 / 26 Overview Two topics for today: Adversarial examples: examples carefully crafted to cause an undesirable behavior (e.g.

412 views • 26 slides
CSC421/2516 Lecture 18: Generative Adversarial Networks Roger Grosse and Jimmy Ba Roger Grosse

CSC421/2516 Lecture 18: Generative Adversarial Networks Roger Grosse and Jimmy Ba Roger Grosse

CSC421/2516 Lecture 18: Generative Adversarial Networks Roger Grosse and Jimmy Ba Roger Grosse and Jimmy Ba CSC421/2516 Lecture 18: Generative Adversarial Networks 1 / 20 Implicit Generative Models Recall: implicit generative models learn a

237 views • 20 slides
LAB MEETING: A Connection Between Generative Adversarial Networks, Inverse Reinforcement Learning

LAB MEETING: A Connection Between Generative Adversarial Networks, Inverse Reinforcement Learning

LAB MEETING: A Connection Between Generative Adversarial Networks, Inverse Reinforcement Learning and Energy-Based models Suwon Suh POSTECH MLG Feb, 13, 2017 Goal Understanding Basic Models 1) Generative Adversarial Networks (GAN) 2)

722 views • 12 slides
Generative Adversarial Networks Stefano Ermon, Aditya Grover Stanford University Lecture 9

Generative Adversarial Networks Stefano Ermon, Aditya Grover Stanford University Lecture 9

Generative Adversarial Networks Stefano Ermon, Aditya Grover Stanford University Lecture 9 Stefano Ermon, Aditya Grover (AI Lab) Deep Generative Models Lecture 9 1 / 22 Recap Model families Autoregressive Models: p ( x ) = n i =1 p

248 views • 22 slides
Generative Adversarial Networks Stefano Ermon, Aditya Grover Stanford University Lecture 9

Generative Adversarial Networks Stefano Ermon, Aditya Grover Stanford University Lecture 9

Generative Adversarial Networks Stefano Ermon, Aditya Grover Stanford University Lecture 9 Stefano Ermon, Aditya Grover (AI Lab) Deep Generative Models Lecture 9 1 / 23 Recap Model families Autoregressive Models: p ( x ) = n i =1 p

392 views • 23 slides
Introduction to Generative Adversarial Networks Ian Goodfellow, OpenAI Research Scientist NIPS

Introduction to Generative Adversarial Networks Ian Goodfellow, OpenAI Research Scientist NIPS

Introduction to Generative Adversarial Networks Ian Goodfellow, OpenAI Research Scientist NIPS 2016 Workshop on Adversarial Training Barcelona, 2016-12-9 Adversarial Training A phrase whose usage is in flux; a new term that applies to both

792 views • 25 slides
Robust Statistics and Generative Adversarial Networks Yuan YAO HKUST Chao Gao (Chicago) Jiyu

Robust Statistics and Generative Adversarial Networks Yuan YAO HKUST Chao Gao (Chicago) Jiyu

Robust Statistics and Generative Adversarial Networks Yuan YAO HKUST Chao Gao (Chicago) Jiyu Liu (Yale) Weizhi Zhu (HKUST) Deep Learning is Notoriously Not Robust! Imperceivable adversarial examples are ubiquitous to fail neural networks

1.8k views • 133 slides
Generative Adversarial Networks presented by Ian Goodfellow presentation co-developed with Aaron

Generative Adversarial Networks presented by Ian Goodfellow presentation co-developed with Aaron

Generative Adversarial Networks presented by Ian Goodfellow presentation co-developed with Aaron Courville 1 In todays talk Generative Adversarial Networks Goodfellow et al., NIPS 2014 Conditional Generative

2.84k views • 40 slides
Genera&ve Adversarial Networks NTT

Genera&ve Adversarial Networks NTT

Genera&ve Adversarial Networks NTT 2020 6 10 @JSAI2020 KS-02 AI MIRU2020

899 views • 70 slides
Generative Adversarial Networks Aaron Mishkin UBC MLRG 2018W2 1 Generative Adversial Networks

Generative Adversarial Networks Aaron Mishkin UBC MLRG 2018W2 1 Generative Adversial Networks

Generative Adversarial Networks Aaron Mishkin UBC MLRG 2018W2 1 Generative Adversial Networks Two imaginary celebrities that were dreamed up by a random number generator. https://research.nvidia.com/publication/2017-10

665 views • 39 slides
Adversarial Examples and Adversarial Training Ian Goodfellow, OpenAI Research Scientist Security

Adversarial Examples and Adversarial Training Ian Goodfellow, OpenAI Research Scientist Security

Adversarial Examples and Adversarial Training Ian Goodfellow, OpenAI Research Scientist Security Seminar, Stanford University, 2017-01-17 In this presentation Intriguing Properties of Neural Networks Szegedy et al, 2013

939 views • 44 slides
Robust Statistics and Generative Adversarial Networks Yuan YAO HKUST 1 Chao GAO Jiyi LIU

Robust Statistics and Generative Adversarial Networks Yuan YAO HKUST 1 Chao GAO Jiyi LIU

Robust Statistics and Generative Adversarial Networks Yuan YAO HKUST 1 Chao GAO Jiyi LIU Weizhi ZHU U. Chicago Yale U. HKUST 2 Deep Learning is Notoriously Not Robust! Imperceivable adversarial examples are ubiquitous to fail

833 views • 72 slides
CVPR 2020 Universal Adversarial Attacks Image agnostic and transferable across networks

CVPR 2020 Universal Adversarial Attacks Image agnostic and transferable across networks

1 1,4 2,3 2 3 4 1 CVPR 2020 Universal Adversarial Attacks Image agnostic and transferable across networks Adversarial No defense: Baseline DNN perturbation Input image Classification Feature extraction dense Perturbed image

592 views • 6 slides

More recommend