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What are the implications of this with self driving cars or biometric security devices? Also.. In the "incomprehensible noise" pattern classes, I can pretty c
by irascible 12y ago
What are the implications of this with self driving cars or
biometric security devices?
Also.. In the "incomprehensible noise" pattern classes, I can pretty clearly see features that I would identify as a robin, cheetah, armadillo, and lesser panda. I don't think this means the classifiers are being fooled, they are just not being trained to recognize the class of "noisy image that looks kind of like something but is actally noise"
- wodenokoto 12y agoI think the required noise / subspaces are unlikely to be met in real world scenarios. That is, you won't take a picture of a spider where the sun just happens to shine in the right way and your lens is in just the right angle to make an image recognized as a giraf. It may become an attack vector, but one the is very difficult to fit to the classifier you are targeting (your car may be using online learning, so there is a chance that an image generated on one self driving car won't work on another of the same model) then you have to show this carefully generated image to the car, but if you show it to the camera, it will already be distorted beyond its fooling capability. due to angle, lighting, lenses etc.