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"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

by Dowwie 10y ago

"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

echelon 10y ago
I wonder if such "patterns" could find use in clothing or as bumper stickers. I can envision this sort of thing taking off as a counter-culture, or anti-technology social weapon. It certainly wouldn't be hard to produce and iterate on. I imagine it would be hard to enforce, let alone legislate against subtle visual cues that trigger machine vision signals. Interesting times lie ahead...
- Dowwie 10y ago
  or water transfer printed "makeup"
- rhcom2 10y ago
  People have been using a similar idea to counter facial recognition. https://cvdazzle.com/ https://cvdazzle.com/
  - sp332 10y ago
    More recently, CMU made glasses that can make you show up as someone else. http://qz.com/823820/carnegie-mellon-made-a-special-pair-of-glasses-that-lets-you-steal-a-digital-identity/ http://qz.com/823820/carnegie-mellon-made-a-special-pair-of-...
- deleted 10y ago
  [deleted]
- __jal 10y ago
  About 10 years ago, when I learned about the EURion Constellation[1], I made a T-shirt with the pattern on it. It sort-of worked - a photo taken at the right distance would frequently cause a copy machine to refuse to copy it. It was twitchy, though. 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. [1] https://en.wikipedia.org/wiki/EURion_constellation https://en.wikipedia.org/wiki/EURion_constellation
- deepnotderp 10y ago
  The AI can be trained to become robust to Adversarial examples. Not only that, there is work going on right now that aims to recognize all Adversarial examples. Early results are promising. I fear for the future if the fascists control the neural networks.
  - lerid 10y ago
    Did you read the article? > 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.
    - deepnotderp 10y ago
      Ah, haven't kept up with Adversarial examples research lately,sorry.
idreyn 10y ago
Do current self-driving systems use vision as ground truth about the presence of things like stop signs? I would hope that the cameras would be used to augment and perhaps feed back into a street map with this information, and that cars would come to a stop at ambiguous intersections.
- tintor 10y ago
  You definitely can't expect map data to be reliable enough to be the only source of truth for something as important as stop signs.
  - PeterisP 10y ago
    Yeah, but these can definitely be used as complementary sources - with proper integration, they'd match almost always, but all mismatches seem like you should assume that the sign is there; e.g. if a sign is in map but not in DB, then it's likely the sign is obscured by something but still should be followed; and if there is a sign in vision but not in DB, then it's likely to be part of some temporary road works. The scenarios where the sign truly "isn't" there are possible, but comparably much more rare.
- Cyphase 10y ago
  Potentially they could also just watch for cars on visible cross-streets; even in otherwise ideal conditions, it would be good if your autonomous car didn't keep going when it has the right of way if it can see a car about to barrel through a stoplight from the side.
  - kaspm 10y ago
    This brings up a point I've thought about quite a bit with autonomous vehicles. I studied transportation planning in college and one of the tenants is that human drivers are on a spectrum between aggressive and conservative about finding efficiencies in high traffic situations. The canonical example is that if a lane next to you is going significantly faster, an aggressive driver will perform a (relatively) unsafe maneuver to cut off drivers in the faster lane and change lanes. 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.
huac 10y ago
Already exists - "The triangles are known variously as 3-D, virtual or just plain fake speed humps." https://nudges.wordpress.com/2008/07/14/another-visual-trick-to-nudge-drivers-to-slow-down/ https://nudges.wordpress.com/2008/07/14/another-visual-trick...
- mattkrause 10y ago
  The comment there suggests calling them “bump l’œil”, which is genius.
- nneonneo 10y ago
  Humans will quickly learn that these things are fake and just drive over them at full speed, though. It might stop people new to the area, but not the majority of people who live there.
  - huac 10y ago
    I think that's reasonable but perhaps not that important. In a school zone, the neighborhood people presumably send their kids there/have friends kids there, and are likely to slow down anyways. Even if you don't, it could be a useful reminder given that it's a school zone to really take it slow. It's not a perfect solution but with how much cheaper it is perhaps still viable.
Cyphase 10y ago
Everyone can have custom-tailored hoodies that cause the human recognition systems to identify them as Luke Cage.
saurik 10y ago
While these modifications are fascinating for being something that humans can't readily notice, the underlying attack is something you can also trivially perform against a human driver by covering, removing, or altering a stop sign such that it no longer looks like a stop sign, and it will be much more effective than trying to trick self-driving cars. 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.
- r00fus 10y ago
  > covering, removing, or altering a stop sign such that it no longer looks like a stop sign Isn't that illegal in most jurisdictions?
  - saurik 10y ago
    I would rather expect the same laws that keep you from defacing or removing a stop sign would apply to modifying one with noise to make it invisible to self-driving cars.
    - tcheard 10y ago
      Although, you could argue that an attacker who is targeting self-driving cars could modify the sign in a way that is unnoticeable to humans, but greatly affects how the self-driving car interprets the sign. 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.
- aetherson 10y ago
  I don't think it would be "much more effective" than trying to trick self-driving cars. Humans are (still, much) better at reasoning their way through ambiguous sensory input based on their abstract understanding of the world than AIs are. 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.
aperrien 10y ago
The same sort of tactic can be done with humans: https://www.youtube.com/watch?v=G_pAcIjqcuY https://www.youtube.com/watch?v=G_pAcIjqcuY Nothing (and likely on-one) is foolproof.
aub3bhat 10y ago
Attackers can also shine bright laser pointers at aircrafts. Thankfully we have cops to stop this kind of vandalism effectively.
xapata 10y ago
Ever seen a video of some kids pulling the "invisible rope prank"? The AI doesn't need to be perfect, just better than humans.
- ktta 10y ago
  But the problem is that AI can fail in unexpected ways. 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?
  - rrobukef 10y ago
    So, it just needs to be (significantly) better than the average human. I think it's even better because each time an unexpected crash happens all cars can learn. We can't even teach people not to drive drunk. Is it that scary to not know how and when it can crash? You don't think about being blindsided at each intersection...
  - xapata 10y ago
    Good unit testing includes maliciously constructed inputs.

by Dowwie 10y ago

echelon 10y ago

I wonder if such "patterns" could find use in clothing or as bumper stickers. I can envision this sort of thing taking off as a counter-culture, or anti-technology social weapon. It certainly wouldn't be hard to produce and iterate on. I imagine it would be hard to enforce, let alone legislate against subtle visual cues that trigger machine vision signals. Interesting times lie ahead...

Dowwie 10y ago
or water transfer printed "makeup"
rhcom2 10y ago
People have been using a similar idea to counter facial recognition. https://cvdazzle.com/ https://cvdazzle.com/
- sp332 10y ago
  More recently, CMU made glasses that can make you show up as someone else. http://qz.com/823820/carnegie-mellon-made-a-special-pair-of-glasses-that-lets-you-steal-a-digital-identity/ http://qz.com/823820/carnegie-mellon-made-a-special-pair-of-...
deleted 10y ago
[deleted]
__jal 10y ago
About 10 years ago, when I learned about the EURion Constellation[1], I made a T-shirt with the pattern on it. It sort-of worked - a photo taken at the right distance would frequently cause a copy machine to refuse to copy it. It was twitchy, though. 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. [1] https://en.wikipedia.org/wiki/EURion_constellation https://en.wikipedia.org/wiki/EURion_constellation
deepnotderp 10y ago
The AI can be trained to become robust to Adversarial examples. Not only that, there is work going on right now that aims to recognize all Adversarial examples. Early results are promising. I fear for the future if the fascists control the neural networks.
- lerid 10y ago
  Did you read the article? > 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.
  - deepnotderp 10y ago
    Ah, haven't kept up with Adversarial examples research lately,sorry.

idreyn 10y ago

Do current self-driving systems use vision as ground truth about the presence of things like stop signs? I would hope that the cameras would be used to augment and perhaps feed back into a street map with this information, and that cars would come to a stop at ambiguous intersections.

tintor 10y ago
You definitely can't expect map data to be reliable enough to be the only source of truth for something as important as stop signs.
- PeterisP 10y ago
  Yeah, but these can definitely be used as complementary sources - with proper integration, they'd match almost always, but all mismatches seem like you should assume that the sign is there; e.g. if a sign is in map but not in DB, then it's likely the sign is obscured by something but still should be followed; and if there is a sign in vision but not in DB, then it's likely to be part of some temporary road works. The scenarios where the sign truly "isn't" there are possible, but comparably much more rare.
Cyphase 10y ago
Potentially they could also just watch for cars on visible cross-streets; even in otherwise ideal conditions, it would be good if your autonomous car didn't keep going when it has the right of way if it can see a car about to barrel through a stoplight from the side.
- kaspm 10y ago
  This brings up a point I've thought about quite a bit with autonomous vehicles. I studied transportation planning in college and one of the tenants is that human drivers are on a spectrum between aggressive and conservative about finding efficiencies in high traffic situations. The canonical example is that if a lane next to you is going significantly faster, an aggressive driver will perform a (relatively) unsafe maneuver to cut off drivers in the faster lane and change lanes. 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.

huac 10y ago

Already exists - "The triangles are known variously as 3-D, virtual or just plain fake speed humps." https://nudges.wordpress.com/2008/07/14/another-visual-trick-to-nudge-drivers-to-slow-down/ https://nudges.wordpress.com/2008/07/14/another-visual-trick...

mattkrause 10y ago
The comment there suggests calling them “bump l’œil”, which is genius.
nneonneo 10y ago
Humans will quickly learn that these things are fake and just drive over them at full speed, though. It might stop people new to the area, but not the majority of people who live there.
- huac 10y ago
  I think that's reasonable but perhaps not that important. In a school zone, the neighborhood people presumably send their kids there/have friends kids there, and are likely to slow down anyways. Even if you don't, it could be a useful reminder given that it's a school zone to really take it slow. It's not a perfect solution but with how much cheaper it is perhaps still viable.

Cyphase 10y ago

Everyone can have custom-tailored hoodies that cause the human recognition systems to identify them as Luke Cage.

saurik 10y ago

While these modifications are fascinating for being something that humans can't readily notice, the underlying attack is something you can also trivially perform against a human driver by covering, removing, or altering a stop sign such that it no longer looks like a stop sign, and it will be much more effective than trying to trick self-driving cars. 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.

r00fus 10y ago
> covering, removing, or altering a stop sign such that it no longer looks like a stop sign Isn't that illegal in most jurisdictions?
- saurik 10y ago
  I would rather expect the same laws that keep you from defacing or removing a stop sign would apply to modifying one with noise to make it invisible to self-driving cars.
  - tcheard 10y ago
    Although, you could argue that an attacker who is targeting self-driving cars could modify the sign in a way that is unnoticeable to humans, but greatly affects how the self-driving car interprets the sign. 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.
aetherson 10y ago
I don't think it would be "much more effective" than trying to trick self-driving cars. Humans are (still, much) better at reasoning their way through ambiguous sensory input based on their abstract understanding of the world than AIs are. 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.

aperrien 10y ago

The same sort of tactic can be done with humans: https://www.youtube.com/watch?v=G_pAcIjqcuY https://www.youtube.com/watch?v=G_pAcIjqcuY Nothing (and likely on-one) is foolproof.

aub3bhat 10y ago

Attackers can also shine bright laser pointers at aircrafts. Thankfully we have cops to stop this kind of vandalism effectively.

xapata 10y ago

Ever seen a video of some kids pulling the "invisible rope prank"? The AI doesn't need to be perfect, just better than humans.

ktta 10y ago
But the problem is that AI can fail in unexpected ways. 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?
- rrobukef 10y ago
  So, it just needs to be (significantly) better than the average human. I think it's even better because each time an unexpected crash happens all cars can learn. We can't even teach people not to drive drunk. Is it that scary to not know how and when it can crash? You don't think about being blindsided at each intersection...
- xapata 10y ago
  Good unit testing includes maliciously constructed inputs.