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Adversarial Samples

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Adversarial Samples

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Adversarial Samples

  1. 1. Adversarial Samples Zhedong Zheng University of Technology Sydney 2018-3-31
  2. 2. What?
  3. 3. Define
  4. 4. Formulation
  5. 5. Why? Other Network
  6. 6. Why? Linear Classifier
  7. 7. Why?
  8. 8. Why?
  9. 9. Why? CVPR 2016
  10. 10. How? • 1. The Basic Iteration Method • 2. Fast Method (and Fig) • 3. Least-likely Method
  11. 11. The Basic Iteration Method
  12. 12. Fast Method (and Fig)
  13. 13. Least-likely Method
  14. 14. Compare
  15. 15. Compare
  16. 16. Application
  17. 17. 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

Adversarial Samples

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