[HN Gopher] Images altered to trick machine vision can influence...
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Images altered to trick machine vision can influence humans too
Author : xnx
Score : 31 points
Date : 2024-01-02 20:30 UTC (2 hours ago)
(HTM) web link (deepmind.google)
(TXT) w3m dump (deepmind.google)
| xnx wrote:
| Though not mentioned in the blog post, this seems like it would
| have some applications for true "subliminal" advertising.
| tudorw wrote:
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| stefs wrote:
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| wizzwizz4 wrote:
| I don't see how this is surprising. The noise pattern in the
| first figure _looks_ cat-like: I can see the ears, the head, the
| paws, the front half of the body... Having that "seemingly random
| pattern" to trace over would probably let me sketch a cat,
| something I can 't normally do without a reference. (Though, the
| face is muddled and in the wrong place - almost like it's a cat
| collage - so I might only get the outline of a cat.)
|
| This study's result is implied by the stronger result: "a neural
| network's notion of a category sometimes resembles members of the
| category". I'm sure a competent sketch artist could yield similar
| or better results, being able to take advantage of peculiarities
| of the human visual system. (In fact, that might be a good
| follow-up study: I might claim it if nobody else does.)
| falcolas wrote:
| Maybe my brain's just odd... but I don't see the cat-like
| figure in the noise. I just see noise. There are a few edges,
| but nothing really cat-like to my mind.
|
| My subconscious pattern recognition for faces and such has
| always been weak, fwiw.
| graypegg wrote:
| One thing I'd be curious about is how many of these perturbations
| can you stack up?
|
| A picture of a cat still retains an obvious cat-ness to humans,
| even when it's been tainted by "truck-ness", but they're slightly
| more likely to agree it's more truck-y. That response scales with
| how perturbed it is. (Fig 3 in paper.) If you added the
| perturbations for "vehicle-ness", would that response be stronger
| while affecting the image less than cracking up the intensity on
| the effect? Could you start combining separate concepts, and pick
| them out individually as like... "activation scores" or
| something?
|
| If so, feels like compression all of a sudden. I know there's a
| ton of other ML compression things out there, but that just feels
| like it could be really information dense.
| Habgdnv wrote:
| I copied the image on the right into all possible AIs that i
| found on the net. They all told the that it is a vase with
| flowers. Even the most primitive. Something seems off here. Maybe
| they trained a model that is able to see "hidden" patterns and
| then they found that they can influence its mind with hidden
| patterns. For the rest of the general population (both humans and
| AIs) both images are the same.
| graypegg wrote:
| Compression possibly? I'm seeing .webps on the paper. Putting
| them into webpinfo gives me: File:
| /.../41467_2023_40499_Fig3_HTML.png.webp RIFF HEADER:
| File size: 671810 Chunk VP8 at offset 12, length
| 671798 Width: 2000 Height: 2255
| Alpha: 0 Animation: 0 Format: Lossy (1)
| No error detected.
|
| So since they're lossy, maybe the subtly is lost?
|
| Edit: The image on the article itself is an SVG, containing 3
| jpegs. So that's absolutely mangled in comparison to the
| paper's lossy images.
|
| https://deepmind.google/api/blob/website/images/Figure0_svg....
| sweezyjeezy wrote:
| I'm not sure that screen-shotting the image will work FWIW -
| any rescaling interpolation in rendering the image on the page
| or loading it for a model will likely reduce or nullify the
| effect.
|
| Also these perturbation based adversarial attacks are often
| model specific. You take the model's gradient at each pixel and
| iteratively perturbate the image to make it more and more
| confident that it's e.g. a cat.
| glitchc wrote:
| This is a poor bit of research. The question "is it more cat-
| like?" Is leading as it specifically instructs the participant to
| look for cat-like features. The experimenters neglect to
| establish the null hypothesis.
| dr_dshiv wrote:
| They made stimuli to be either cat-like or sheep-like, for
| instance, and asked them to pick the more cat-like. It wasn't
| between cat and nothing.
| paxys wrote:
| Very interesting results. There's a massive overlap between the
| current generation of AI research/development and neuroscience,
| and it's fitting that by so desperately trying to make a computer
| intelligent we are unexpectedly learning more about how our own
| brains work.
| nomel wrote:
| > we are unexpectedly learning
|
| Many expected this, decades ago. But, also, many claimed that
| neural networks have nothing to do with the brain. I think
| we're slowly inching towards and understanding that we're the
| result of some fundamentals of information organization, and
| those fundamentals are realized in biology, rather than come
| from it. Those fundamentals are now showing themselves in
| silicon.
| TehShrike wrote:
| In case you were wondering what N was, their first experiment
| involved 16 undergrads psych students and the second experiment
| involved 12.
|
| https://link.springer.com/article/10.3758/BF03206939
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