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

Apply Changes to Eyeglasses

11 of 21

11 of 21

Apply Changes to Eyeglasses

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

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

Automatically move to the next video in the Classroom when playback concludes

  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

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.