"attackers could target autonomous vehicles by using stickers or paint to create an adversarial stop sign that the vehicle would interpret as a 'yield' or other sign"
yeah, this article needs to go to the top of HN and stay there for a while
yeah, this article needs to go to the top of HN and stay there for a while
In the case of a human not seeing a stop sign, some drivers will remember a stop sign used to be at that location, but a self-driving car is less likely to forget and might (in a world with networked cars, which I find slightly scary for different reasons than this) be able to "remember" that other cars had previously seen a stop sign at that location.
Isn't that illegal in most jurisdictions?
Eg. look at the panda images at the start of the linked post. The third image looks like a panda to humans (and most humans probably wouldn't be able to see the difference between the first and third images), but it greatly affects the machine learning interpretation.
By modifying the sign in this way, if it is possible, it would be much harder to detect and enforce against than it would be if the attacker was targeting human drivers.
While an AI might specifically already know that a given intersection is supposed to be controlled, there are large numbers of driving situations in which memory (or maps) aren't very useful in dealing with.
When it comes to self-driving cars, probably the way around attacks on signs or other static road features is just to fail safe even in situations in which you're mis-directed by road features (that is, you drive cautiously enough that even if you don't understand the road situation, you can find a way to stop without hurting anyone). But that's not a general AI solution, and may not even be a general self-driving car solution.
Deep neural nets should really be called "really complicated composition of differentiable functions" and all the training algorithms should really be called "really complicated function root finding" because that's all these things are at the end of the day, a rats nest of functions with a billion knobs.
Or quite possibly we just don't know how to construct the noise yet :P
That's not the reason at all.
It's because if you add exactly the right type of specially crafted noise to an image of a stop sign such that, to a human, it looks like a panda--then who is going to be the arbiter that decides "No, it's still actually really an image of a stop sign and not a panda". The machine?
The real reason is the subjective nature of the perception of reality. With the advent of AI, all sorts of schools of philosophy that used to seem like useless navel-gazing are going to find some real important practical applications real soon. This is metaphysics and ontology. On a similar note (not in TFA), in the "friendly AI" debate, we're going to be looking real hard at the foundations of the philosophy of ethics (where does it really come from?), not just the teleological vs deontological (etc) debates we use to reason about law, politics, governance and appeasement of our "justice" instincts.
An image that triggers a false positive within the AI won't be noticeable to the average human, however good at detecting bullshit as they are. It probably looks like some annoying abstract pattern at the worst.
To the car, however, it could represent anything that made it react adversely upon observing it. Seems like figuring out how to find those images might pose a challenge.
https://nudges.wordpress.com/2008/07/14/another-visual-trick...
I imagine it would be hard to enforce, let alone legislate against subtle visual cues that trigger machine vision signals.
Interesting times lie ahead...
At the time, I was thinking of posing for my DMV photo with it on, because I thought it was interesting and kinda funny. Failed to do so and never resumed the experiment.
I fear for the future if the fascists control the neural networks.
> We find that both adversarial training and defensive distillation accidentally perform a kind of gradient masking. Neither algorithm was explicitly designed to perform gradient masking, but gradient masking is apparently a defense that machine learning algorithms can invent relatively easily when they are trained to defend themselves and not given specific instructions about how to do so. If we transfer adversarial examples from one model to a second model that was trained with either adversarial training or defensive distillation, the attack often succeeds, even when a direct attack on the second model would fail. This suggests that both training techniques do more to flatten out the model and remove the gradient than to make sure it classifies more points correctly.
The AI doesn't need to be perfect, just better than humans.
Humans are much worse than AI when it comes to self-driving tasks like image/pattern recognition, sure. But they are inefficient in expected ways where measures can be taken like having larger and brighter signs, stronger rules, etc. But what happens when you don't know for sure when it can fail and when it won't?
It's been a few years since people are seeing the magic of machine learning but something like this was just discovered. Are you sure if someone goes in front of a Tesla with a picture like the parent comment quoted, it wouldn't crash and cause harm to the people inside?
Is it that scary to not know how and when it can crash? You don't think about being blindsided at each intersection...
IIRC, significant research has been performed to identify whether the resulting lane change is net positive (frees space in the slower lane) or net negative (causes cut off car to slow down).
If autonomous vehicles prioritize safety over efficiency by stopping if there's an ambiguous intersection, will that have a net negative effect on traffic while all these self-driving cars slow down for each other.
https://www.youtube.com/watch?v=G_pAcIjqcuY
Nothing (and likely on-one) is foolproof.