Basic Introduction to Adversarial machine learning, It has topics like What is ML? What is Adversarial ML? How does one generate and manipulate models?

1. Adversarial
Machine Learning

2. What is Machine
Learning?
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3. “
Machine learning is an application
of artificial intelligence (AI)
that provides systems the ability
to automatically learn and improve
from experience without being
explicitly programmed.
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4. What is Adversarial
Machine Learning?
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5. “
Adversarial Machine Learning is a
technique employed in the field of
machine learning which attempts to
fool models through malicious
input.
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6. Adversarial Images
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7. All about Adversarial Images
◎What are they ?
◎Why are they dangerous ?
◎How are they generated ?
◎How can we overcome ?
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8. What are Adversarial Images ?
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9. Why are they dangerous ?
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10. Not Convinced ?
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11. We can make them predict
whatever we want
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13. How are they generated ?
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14. Generate on your own model and
Data -> They can attack many other systems
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15. Generate on your own model and
Data -> They can attack many other systems
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16. Generate on your own model and
Data -> They can attack many other systems
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17. Generate on your own model and
Data -> They can attack many other systems
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18. Generate on your own model and
Data -> They can attack many other systems
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19. Universal Perturbations
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20. How to overcome ?
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21. How to overcome ?
◎Simple empirical observation(Not Effective)
◎Learn a robust model via Adversarial
training
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22. Empirical Methods
◎Foveation - Cropping
◎CNNs are robust to scale,
occlusion,translation, etc.
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23. Empirical Methods
◎Image Blurring.
◎Observed to reduce
the fooling rate.
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24. Adversarial Training
◎Train using adversarial images (x*)
◎At each iteration
-Compute adversarial images for some
batch numbers
-Train using both normal and adversarial
images
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25. Adversarial Training
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26. Adversarial Training
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27. Adversarial Training
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28. Adversarial Training
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29. Adversarial Training
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30. Adversarial Training - Drawbacks
◎Doubles the training time
◎Might be specific to attack
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31. Conclusion
◎We have built systems that are very good
but easily broken
◎Need rigorous understanding of the models
and training procedure.
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32. Thanks!
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