Accessorize to a Crime - Real and Stealthy Attacks on State-Of-The-Art Face Recognition

Accessorize to a Crime - Real and Stealthy Attacks on State-Of-The-Art Face Recognition

ACM CCS via YouTube Direct link

Smooth Transitions Natural images tend to be smooth

13 of 21

13 of 21

Smooth Transitions Natural images tend to be smooth

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Accessorize to a Crime - Real and Stealthy Attacks on State-Of-The-Art Face Recognition

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  1. 1 Intro
  2. 2 Machine Learning Is Ubiquitous
  3. 3 What Do You See?
  4. 4 The Difference
  5. 5 What Are the Adversary's Capabilities? To generate attacks, attacker needs to know how changing input affects output
  6. 6 What's a (Deep) Neural Network?
  7. 7 Face Recognition . Applications: surveillance, access control...
  8. 8 Face Recognition: Our Attacks
  9. 9 Deep Face Recognition
  10. 10 Apply Changes to Face Only
  11. 11 Apply Changes to Eyeglasses
  12. 12 Experiments in Digital Environment
  13. 13 Smooth Transitions Natural images tend to be smooth
  14. 14 Printable Eyeglasses Chalenge: Cannot print all colors
  15. 15 Robust Perturbations
  16. 16 Putting All the Pieces Together - Physically realizable impersonation
  17. 17 Does This Work?
  18. 18 Experiment: Realized Impersonations
  19. 19 Impersonation Attacks Pose Real Risk!
  20. 20 Extensions (See Paper)
  21. 21 Conclusions

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