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