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The fact humans can easily see through the disguise means there are huge glaring faults in the recognition algorithm. These types of attacks will probably be wi

The fact humans can easily see through the disguise means there are huge glaring faults in the recognition algorithm. These types of attacks will probably be wiped away one day with a single breakthrough. You can't fool a human by covering up a small percentage of something with noise. Humans can recognize parts of a face pretty well, classifying smaller parts of an image separately may help defend against this (and also be able to detect composite images, for example where eyes and mouth of different people are combined). Also humans instantly detect that those glasses are noise, there probably needs to be a filter that removes obvious noise before the face recognition begins.

rawnlq 10y ago
On the flip side, I wonder how much of face recognition failures by humans would be considered "huge glaring faults" to machines? For a famous example, take the https://en.wikipedia.org/wiki/Thatcher_effect https://en.wikipedia.org/wiki/Thatcher_effect (where a face will look normal upside down even if you individually flip the eyes and mouth). Or the https://en.wikipedia.org/wiki/Hybrid_image https://en.wikipedia.org/wiki/Hybrid_image (where you see two different images depending on how far you are standing). I bet machines can or eventually handle those cases better than us. Is trying to emulate our own recognition system the best possible approach? It might end up inheriting all our edge cases as well. (For another example relevant to halloween, with a bit of face paint you can fool any human: http://gfycat.com/DeliriousEdibleAustraliansilkyterrier http://gfycat.com/DeliriousEdibleAustraliansilkyterrier. But you can totally imagine a sufficiently advanced algorithm that is capable of ignoring color information and reconstruct a 3d surface for comparison instead.)
- unabridged 10y ago
  >Is trying to emulate our own recognition system the best possible approach? It might end up inheriting all our edge cases as well. I agree, I wouldn't want to emulate human recognition too closely. But I think humans have a bunch of simple heuristics that have yet to be modeled, that can sit in front of heavy ML algorithms. First I think there should be a garbage/unreal filter that all images have to pass through, this would eliminate all those static-y looking adversarial images that can pass for pandas or whatever.
- dom0 10y ago
  > with a bit of face paint you can fool any human While impressive makeup it's still fairly obvious - perhaps not on a still image, but on a video it is, and in real life it will be more obvious still. It took me maybe half a second in the video to notice that it's not a doubled face, but painted on. Hmm... maybe that's not so bad for a metric - the "fooling duration".
- TrainedMonkey 10y ago
  Regarding Thatcher effect, one of the Hinton lectures from https://www.coursera.org/learn/neural-networks https://www.coursera.org/learn/neural-networks talks about difficulties in recognition of reflected/upside down objects. I can't easily look it up, so here is my paraphrasing: Humans exhibit a comparable delay when recognizing and reasoning normal and reflected objects. He then gives a few examples such as answering "is this a left or a right foot shoe" and suggests that brains do rotational transformations as part of the object recognition. The machine solution is probably similar to a human solution - do a bunch of transformations and pick highest confidence one. The interesting part that is humans can answer "this is a shoe" question immediately, but take longer to reason about reflected images.
theoh 10y ago
"a single breakthrough?" This idea demands extraordinary evidence. I reckon most of us can contrive a test case in which any attempt at facial recognition "fails" unless you have a full AI of human-equivalent capabilities. Consider https://en.m.wikipedia.org/wiki/Delusional_misidentification_syndrome https://en.m.wikipedia.org/wiki/Delusional_misidentification...
duaneb 10y ago
This pairs well with this discussion from four days ago: https://news.ycombinator.com/item?id=12824380 https://news.ycombinator.com/item?id=12824380 There are obviously glaring holes in HOW the features are generated and recognized.
didibus 10y ago
We're trained on an insane amount of training data though. It could simply be a matter of more training data required.
BickNowstrom 10y ago
Since the added noise is not just noise, but especially engineered perturbations to maximally activate the output neurons for a certain class -- if you want to draw a comparison to humans, one could look at the hyperstimulus/supernormal stimulus. https://en.wikipedia.org/wiki/Supernormal_stimulus https://en.wikipedia.org/wiki/Supernormal_stimulus Vilayanur S. Ramachandran proposes that the hyperstimulus is responsible for exaggerated forms in (primitive) art, so to maximally stimulate our brain: https://www.youtube.com/watch?v=7ZTvHqM-_jE https://www.youtube.com/watch?v=7ZTvHqM-_jE Further to fringe, this sci-fi short story about deviously crafted basilisk images that crash the human brain: http://www.infinityplus.co.uk/stories/blit.htm http://www.infinityplus.co.uk/stories/blit.htm > This first example of the Berryman Logical Image Technique (hence the usual acronym BLIT) evolved from AI work at the Cambridge IV supercomputer facility, now discontinued. V.Berryman and C.M.Turner hypothesized that pattern-recognition programs of sufficient complexity might be vulnerable to "Gödelian shock input" in the form of data incompatible with internal representation. Berryman went further and suggested that the existence of such a potential input was a logical necessity.
Asooka 10y ago
I feel like they will just train a separate NN to recognise "weird glasses", run it in parallel, and arrest (or ask to leave, etc.) everyone that is recognised as wearing weird glasses and isn't recognised as a person.
sangnoir 10y ago
> Humans can recognize parts of a face pretty well, classifying smaller parts of an image separately may help defend against this Even humans can be "attacked": this infamous image (http://i.imgur.com/fkYaYEr.jpg http://i.imgur.com/fkYaYEr.jpg) gives me a headache if I look at it for too long[1]. I wonder how machines will fare when faced with the same concept via convincing makeup... 1. I suspect my brain is telling my eyes that they are out of focus and continually engages the eye muscles to try and bring the image "into focus"

rawnlq 10y ago

On the flip side, I wonder how much of face recognition failures by humans would be considered "huge glaring faults" to machines? For a famous example, take the https://en.wikipedia.org/wiki/Thatcher_effect https://en.wikipedia.org/wiki/Thatcher_effect (where a face will look normal upside down even if you individually flip the eyes and mouth). Or the https://en.wikipedia.org/wiki/Hybrid_image https://en.wikipedia.org/wiki/Hybrid_image (where you see two different images depending on how far you are standing). I bet machines can or eventually handle those cases better than us. Is trying to emulate our own recognition system the best possible approach? It might end up inheriting all our edge cases as well. (For another example relevant to halloween, with a bit of face paint you can fool any human: http://gfycat.com/DeliriousEdibleAustraliansilkyterrier http://gfycat.com/DeliriousEdibleAustraliansilkyterrier. But you can totally imagine a sufficiently advanced algorithm that is capable of ignoring color information and reconstruct a 3d surface for comparison instead.)

unabridged 10y ago
>Is trying to emulate our own recognition system the best possible approach? It might end up inheriting all our edge cases as well. I agree, I wouldn't want to emulate human recognition too closely. But I think humans have a bunch of simple heuristics that have yet to be modeled, that can sit in front of heavy ML algorithms. First I think there should be a garbage/unreal filter that all images have to pass through, this would eliminate all those static-y looking adversarial images that can pass for pandas or whatever.
dom0 10y ago
> with a bit of face paint you can fool any human While impressive makeup it's still fairly obvious - perhaps not on a still image, but on a video it is, and in real life it will be more obvious still. It took me maybe half a second in the video to notice that it's not a doubled face, but painted on. Hmm... maybe that's not so bad for a metric - the "fooling duration".
TrainedMonkey 10y ago
Regarding Thatcher effect, one of the Hinton lectures from https://www.coursera.org/learn/neural-networks https://www.coursera.org/learn/neural-networks talks about difficulties in recognition of reflected/upside down objects. I can't easily look it up, so here is my paraphrasing: Humans exhibit a comparable delay when recognizing and reasoning normal and reflected objects. He then gives a few examples such as answering "is this a left or a right foot shoe" and suggests that brains do rotational transformations as part of the object recognition. The machine solution is probably similar to a human solution - do a bunch of transformations and pick highest confidence one. The interesting part that is humans can answer "this is a shoe" question immediately, but take longer to reason about reflected images.

theoh 10y ago

"a single breakthrough?" This idea demands extraordinary evidence. I reckon most of us can contrive a test case in which any attempt at facial recognition "fails" unless you have a full AI of human-equivalent capabilities. Consider https://en.m.wikipedia.org/wiki/Delusional_misidentification_syndrome https://en.m.wikipedia.org/wiki/Delusional_misidentification...

duaneb 10y ago

This pairs well with this discussion from four days ago: https://news.ycombinator.com/item?id=12824380 https://news.ycombinator.com/item?id=12824380 There are obviously glaring holes in HOW the features are generated and recognized.

didibus 10y ago

We're trained on an insane amount of training data though. It could simply be a matter of more training data required.

BickNowstrom 10y ago

Since the added noise is not just noise, but especially engineered perturbations to maximally activate the output neurons for a certain class -- if you want to draw a comparison to humans, one could look at the hyperstimulus/supernormal stimulus. https://en.wikipedia.org/wiki/Supernormal_stimulus https://en.wikipedia.org/wiki/Supernormal_stimulus Vilayanur S. Ramachandran proposes that the hyperstimulus is responsible for exaggerated forms in (primitive) art, so to maximally stimulate our brain: https://www.youtube.com/watch?v=7ZTvHqM-_jE https://www.youtube.com/watch?v=7ZTvHqM-_jE Further to fringe, this sci-fi short story about deviously crafted basilisk images that crash the human brain: http://www.infinityplus.co.uk/stories/blit.htm http://www.infinityplus.co.uk/stories/blit.htm > This first example of the Berryman Logical Image Technique (hence the usual acronym BLIT) evolved from AI work at the Cambridge IV supercomputer facility, now discontinued. V.Berryman and C.M.Turner hypothesized that pattern-recognition programs of sufficient complexity might be vulnerable to "Gödelian shock input" in the form of data incompatible with internal representation. Berryman went further and suggested that the existence of such a potential input was a logical necessity.

Asooka 10y ago

I feel like they will just train a separate NN to recognise "weird glasses", run it in parallel, and arrest (or ask to leave, etc.) everyone that is recognised as wearing weird glasses and isn't recognised as a person.

sangnoir 10y ago

> Humans can recognize parts of a face pretty well, classifying smaller parts of an image separately may help defend against this Even humans can be "attacked": this infamous image (http://i.imgur.com/fkYaYEr.jpg http://i.imgur.com/fkYaYEr.jpg) gives me a headache if I look at it for too long[1]. I wonder how machines will fare when faced with the same concept via convincing makeup... 1. I suspect my brain is telling my eyes that they are out of focus and continually engages the eye muscles to try and bring the image "into focus"