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ON THE ADVERSARIAL ROBUSTNESS OF UNCERTAINTY AWARE DEEP NEURAL - PowerPoint PPT Presentation

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ON THE ADVERSARIAL ROBUSTNESS OF UNCERTAINTY AWARE DEEP NEURAL NETWORKS APRIL 29 TH , 2019 PREPARED BY: ALI HARAKEH QUESTION Can a neural network mitigate the effects of adversarial attacks by estimating the uncertainty in its predictions ?

  1. ON THE ADVERSARIAL ROBUSTNESS OF UNCERTAINTY AWARE DEEP NEURAL NETWORKS APRIL 29 TH , 2019 PREPARED BY: ALI HARAKEH

  2. QUESTION Can a neural network mitigate the effects of adversarial attacks by estimating the uncertainty in its predictions ? 4/29/2019 Ali Harakeh 2

  3. ADVERSARIAL ROBUSTNESS

  4. HOW GOOD IS YOUR NEURAL NETWORK ? • Neural networks are not robust to input perturbations. • Example: Carlini and Wagner Attack on MNIST 3 1 2 0 4/29/2019 Ali Harakeh 4

  5. ADVERSARIAL PERTURBATIONS X 0 X a X b Decision Boundary 1 Decision Boundary 3 Ostrich Vacuum Shoe Minimum Perturbation X a X 0 X b Decision Boundary 2 4/29/2019 Ali Harakeh 5

  6. UNCERTAINTY IN DNNS

  7. SOURCES OF UNCERTAINTY IN DNNS • Two sources of uncertainty exist in DNNs. • Epistemic (Model) Uncertainty: Captures the ignorance about which model generated our data. • Aleatoric (Observation) Uncertainty: Captures the inherent noise in the observations. Original Image Epsitemic Uncertainty Aleatoric Uncertainty 4/29/2019 Ali Harakeh 7

  8. CAPTURING EPISTEMIC UNCERTAINTY • Marginalizing over neural network parameters: Conv3-64 Conv3-64 Conv3-64 Soft Max FC-10 4/29/2019 Ali Harakeh 8

  9. CHANGE IN DECISION BOUNDARIES X 0 X a X b Decision Boundary 1 Decision Boundary 3 Ostrich Vacuum Shoe X a X 0 X b Decision Boundary 2 4/29/2019 Ali Harakeh 9

  10. CAPTURING ALEATORIC UNCERTAINTY • Heteroscedastic variance estimation: Conv3-64 Conv3-64 Conv3-64 Soft Max FC-10 4/29/2019 Ali Harakeh 10

  11. CHANGE IN DATA POINT X 0 X a X b Decision Boundary 1 Decision Boundary 3 Ostrich Vacuum Shoe X a X 0 X b Decision Boundary 2 4/29/2019 Ali Harakeh 11

  12. METHODOLOGY

  13. 13 Soft Max Soft Max Average Pool FC-10 Conv3-10 Conv3-64 Average Pool Conv3-64 Conv3-64 NEURAL NETWORKS AND DATASETS Conv3-64 Ali Harakeh Conv3-64 ConvNet On CIFAR10 ConvNet On MNIST 4/29/2019

  14. 14 Soft Max Soft Max Average Pool FC-10 Dropout Conv3-10 Conv3-64 Average Pool Dropout EPISTEMIC UNCERTAINTY: AN APPROXIMATION Dropout Conv3-64 Conv3-64 Dropout Dropout Conv3-64 Ali Harakeh Conv3-64 ConvNet On CIFAR10 ConvNet On MNIST 4/29/2019

  15. 15 Soft Max Soft Max Sampler Sampler Average Pool Average Pool FC-10 FC-10 Conv3-10 Conv3-10 Conv3-64 Average Pool Conv3-64 Conv3-64 Conv3-64 Conv3-64 ALEATORIC UNCERTAINTY ESTIMATION Ali Harakeh ConvNet On CIFAR10 ConvNet On MNIST 4/29/2019

  16. GENERATING ADVERSARIAL PERTURBATIONS • Use Cleverhans: https://github.com/tensorflow/cleverhans • Adversarial Attacks: Fast Gradient Sign Method (FGSM): Goodfellow et. Al. 1. Jacobian- Based Saliency Map Attacks (JSMA): Paparnot et. Al. 2. Carlini and Wagner Attacks : Carlini et. Al. 3. Black Box Attack : Papernot et. Al. 4. 4/29/2019 Ali Harakeh 16

  17. RESULTS

  18. RESULTS 4/29/2019 Ali Harakeh 18

  19. EPISTEMIC UNCERTAINTY ESTIMATION 4/29/2019 Ali Harakeh 19

  20. ALEATORIC UNCERTAINTY ESTIMATION 4/29/2019 Ali Harakeh 20

  21. BLACK BOX ATTACK 4/29/2019 Ali Harakeh 21

  22. MC-DROPOUT APPROXIMATION 4/29/2019 Ali Harakeh 22

  23. CONCLUSION

  24. QUESTION Can a neural network mitigate the effects of adversarial attacks by estimating the uncertainty in its predictions ? 4/29/2019 Ali Harakeh 24

  25. ANSWER(S) • Adversarial perturbations cannot be distinguished as input noise through aleatoric uncertainty estimation. • Epistemic uncertainty estimation, manifested as Bayesian Neural Networks might be robust to adversarial attacks. • Results inconclusive, due to the lack of mathematical bounds on the approximation through ensembles and MC-Dropout. • Sufficient Conditions for Robustness to Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Network. • https://openreview.net/forum?id=B1eZRiC9YX 4/29/2019 Ali Harakeh 25

  26. CONCLUSION • There is no easy way out of using robustness certification to guarantee safety of deep neural networks. • Even then, the mode of action of a specific type of adversarial attack needs to be taken into consideration. • Research Question : How to certify against black box attacks? 4/29/2019 Ali Harakeh 26

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