1. If that&#x27;s the case perhaps another kind of blurring? &quot;Intriguing properties of neural networks&quot; (<a href="https:&#x2F;&#x2F;arxiv.org&#x2F;pdf&#x2F;1312.6199.pdf" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;pdf&#x2F;1312.6199.pdf</a> page 6) has examples where you get radically different classifications that I don&#x27;t think would occur naturally or survive a blur with some random element, let alone two moving cameras and a sequence of images. As the title says it&#x27;s an intriguing property, not necessarily a huge problem.<p>2. I honestly can&#x27;t think of a situation where this could occur. It&#x27;s the equivalent of kids shining lasers into the eyes of airline pilots, but the kids need a PhD in deep learning and specialised equipment to be able to do it. A hacker doing some update to the software via a network sounds much more plausible than attacking the system through its vision while it&#x27;s traveling.<p>3. This is the real point in the end I guess, this Google presentation (<a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=tiwVMrTLUWg" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=tiwVMrTLUWg</a>) shows that the first autonomous cars to be sold will be very sophisticated with multiple systems and a lot of traditional software engineering. Hopefully LIDAR costs will come down.