Adversarial Samples

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Adversarial Samples

Adversarial Samples
Zhedong Zheng
University of Technology Sydney
2018-3-31

What?

Define

Formulation

Why? Other Network

Why? Linear Classifier

Why?

Why?

Why? CVPR 2016

How?
• 1. The Basic Iteration Method
• 2. Fast Method (and Fig)
• 3. Least-likely Method

The Basic Iteration Method

Fast Method (and Fig)

Least-likely Method

Compare

Compare

Application

Reference
• Adversarial examples in the physical world (ICLR2017 Workshop)
• Ian Speech on CS231n
• DeepFool: a simple and accurate method to fool deep neural
networks (CVPR2016) The idea in this work is close to the orginal
idea. Loop until the predicted label change.
• Learning with a strong adversary (rejected by ICLR2016?) Apply the
spirit of GAN to optimization.
• Explaining and Harnessing Adversarial Examples (ICLR2015) Fast
Gradient Sign Method
• Exploring the space of adversarial images IJCNN

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Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

Manulife - Insurer Transformation Award 2024

Manulife - Insurer Transformation Award 2024

Manulife - Insurer Transformation Award 2024

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

ICT role in 21st century education and its challenges

ICT role in 21st century education and its challenges

ICT role in 21st century education and its challenges

Ransomware_Q4_2023. The report. [EN].pdf

Ransomware_Q4_2023. The report. [EN].pdf

Ransomware_Q4_2023. The report. [EN].pdf

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Adversarial Samples

1. Adversarial Samples Zhedong Zheng University of Technology Sydney 2018-3-31

5. Why? Other Network

6. Why? Linear Classifier

9. Why? CVPR 2016

10.

11. How? • 1. The Basic Iteration Method • 2. Fast Method (and Fig) • 3. Least-likely Method

12. The Basic Iteration Method

13. Fast Method (and Fig)

14. Least-likely Method

17. Application

18. Reference • Adversarial examples in the physical world (ICLR2017 Workshop) • Ian Speech on CS231n • DeepFool: a simple and accurate method to fool deep neural networks (CVPR2016) The idea in this work is close to the orginal idea. Loop until the predicted label change. • Learning with a strong adversary (rejected by ICLR2016?) Apply the spirit of GAN to optimization. • Explaining and Harnessing Adversarial Examples (ICLR2015) Fast Gradient Sign Method • Exploring the space of adversarial images IJCNN