[mitchellgoffpc]

I’m kind of impressed by how smooth it is, actually. If you watch videos of state-of-the-art object localization NNs, they tend to be EXTREMELY jumpy. These neural nets usually operate on only a single frame at a time, at least in the lower layers, so their predictions tend to jump around a lot from frame to frame (especially when the camera is moving!)

[yarg]

It worries me in general though, and I think that a higher level of consistently in the results of the parts of the image that don't significantly change between frames seems like a goal worth pursuing.

I also think that a deeper understanding of the mechanisms and techniques required to reduce jitter might offer some insights into ways of handling adversarial images.

[yarg]

And I do mean insights and not a potential solution. I think it's an issue related to the handling of the spatial discontinuities introduced by the conversion of an effectively continuous reality into a representation consisting of a large number of discrete elements.

I think a more in depth understanding of how to navigate organically introduced discontinuities could provide a baseline against which we can look to combat the maliciously introduced ones.

It most likely won't be enough - since the problem source is an adaptive and intelligent adversary.