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Open Sourcing a Deep Learning Solution for Detecting NSFW Images

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Re: Open Sourcing a Deep Learning Solution for Detecting NSFW Images

#111

Post by vidarh »

plingamp wrote: post #100
darklajid wrote: post #88

They acknowledge that NSFW (or pornographic) is hard to define, a la 'I recognize it if I see it'. But looking at the meager 3 sample images I'm confused about the scoring already. Why is the one in the middle scoring the highest? The question is an honest one. The two rightmost images seem to be interchangeable to me and are ~boring~: People at the beach. Is this network therefor already trained to include the biase…

All ML networks are inherently biased towards its creators. My colleague recently described this issue to me as the "Old, white, male" problem. This is why most voice recognition services drastically fail when they are shown foreign accents.

> This is why most voice recognition services drastically fail when they are shown foreign accents

As someone with a broad Norwegian accent: This has gotten massively better over the last few years.

Not that long ago, my local cinema chain started using voice recognition to discriminate between a list of city names, and it would consistently think I said "Birmingham" when I said "London" (!).

These days, both my Amazon Fire and the Youtube app will correctly recognise most things I throw at it, including e.g. names of random Youtube channels that bear no relation to real English words.

It's by no means perfect, but it's getting there. In relation to the "old, white, male" problem (well, I do somewhat fit that), presumably because these systems are now finally trained on huge and varied data sets.

Re: Open Sourcing a Deep Learning Solution for Detecting NSFW Images

#112

Post by DrStalker »

BinaryIdiot wrote: post #27
zardo wrote: post #22

Earlier quoted context omitted.

As long as what you need is a nightmarish version of the requested scene. What happens here with generating illegal content? If you put a public text->image gan up, and someone uses it to generate child porn, are you responsible? How could you make sure it couldn't?

I would imagine if you're generating then no real people were part of its creation therefore it would be legal. If I remember correctly cartoons of children having sex is not illegal (in the United States as far as I know). Though that then raises the question: when happens when it can be generated so realistically that it looks indistinguishable from the real thing? Would it still be treated like cartoons? How could…

Consult a lawyer first; in some parts of the world constructed images of child pornography are illegal, covering both drawn child pornography and photo-manipulation where a child's head is pasted on to a young-looking but legal woman's body.

Re: Open Sourcing a Deep Learning Solution for Detecting NSFW Images

#113

Post by DrStalker »

WhitneyLand wrote: post #87
zardo wrote: post #77

Earlier quoted context omitted.

>How could you make sure it couldn't? I just realized the answer to this is pretty obvious, you could have a network trained to classify child pornography and use it to censor the output. Getting the training set would be an issue, you would probably have to work with law enforcement to do it.

Impossible. There is no accurate way to determine age by external appearance. For example a 15 year could look older than an 18 year old.

That doesn't stop law makers from trying. Like the mess over small-breasted porn being banned in Australia in 2010:

> While the ACB claims that there is no blanket ban on small breasts as such, women over the age of 18 with small breasts who might look young ARE banned.

http://www.inquisitr.com/59633/australian-government-censor-...

Re: Open Sourcing a Deep Learning Solution for Detecting NSFW Images

#114

Post by eva1984 »

inlined wrote: post #11

Forgive my ignorance of ML but the last bit: "you'll need your own porn to train on" confused me. Does this mean that they're just exposing the rough topology of their neutral net (eg depth) and not the actual weights between nodes? I'm curious to learn from an ML expert how much this actually offers.

You initialized the nets using their weights, and then provide your own data, in this case, a list of images contains a (porn|no-porn) label to 'fine-tune' the nets towards your usage case.

Re: Open Sourcing a Deep Learning Solution for Detecting NSFW Images

#115

Post by RandomBK »

onli wrote: post #51
x1798DE wrote: post #49

Earlier quoted context omitted.

I don't think it's a derivative work just because one of the inputs is copyrighted. I think it's more descriptive than derivative. Content producers don't generally own copyright in critics' descriptions of their movies or of plot summaries, even though their copyrighted material is a necessary input to the description's creation.

Also, good luck trying to prove that your film was used to train this network.

You could be compelled to produce your training set during the discovery phase of a lawsuit.

Re: Open Sourcing a Deep Learning Solution for Detecting NSFW Images

#116

Post by inlined »

bbctol wrote: post #65
pjreddie wrote: post #62

Earlier quoted context omitted.

Yes. You would have to have a large training set with these labels but it would be pretty straightforward to train. You would probably want a tagging model not a classifier because there could be multiple objects of interest in the same image. If you get me the training data I could train a model for you pretty quickly.

And it would be an... interesting job to tag the training set. Although for higher level content, I suppose lots of porn videos have very specific category tags that could be an interesting data set to play with. Uh, to analyze.

Iirc you can use ML to learn the tags as well. They tend to be in text surrounding the media

Re: Open Sourcing a Deep Learning Solution for Detecting NSFW Images

#117

Post by gizmo686 »

gozur88 wrote: post #106
gizmo686 wrote: post #42

Earlier quoted context omitted.

No " (8) “child pornography” means any visual depiction, including any photograph, film, video, picture, or computer or computer-generated image or picture, whether made or produced by electronic, mechanical, or other means, of sexually explicit conduct, where— (A) the production of such visual depiction involves the use of a minor engaging in sexually explicit conduct; (B) such visual depiction is a digital image, c…

Wouldn't that make it illegal under 8B?

Sufficiently realistic digital child porn would be prohibited under 8B. The original context of this conversation was neural net generated images, so I had not considered the case of photorealistic images. Those would be a violation of 8B (if they are of sufficient quality).

Having said that the segment I quoted is only the definition of child porn. The law prohibiting child porn [0, section c2] provides that:

"It shall be an affirmative defense to a charge of violating paragraph (1), (2), (3)(A), (4), or (5) of subsection (a) that— ...

the alleged child pornography was not produced using any actual minor or minors. No affirmative defense under subsection (c)(2) shall be available in any prosecution that involves child pornography as described in section 2256(8)(C)"

It is worth mentioning the segments of section A that are excluded from this defence:

Section 3B prohibts the advertisement/distribution/solicitation/etc of of material that is claimed to contain (i) "an obscene visual depiction of a minor engaging in sexually explicit conduct; or (ii) a visual depiction of an actual minor engaging in sexually explicit conduct;"

The relevant part of this is (i), where you would need to parse out the definition of "obscene" and "minor". Section 2256 defines minor as "any person under the age of eighteen years", however the courts would probably read it in this context in contrast to the phrase "actual minor". I could not fine the definition of "obscene" or "actual minor". Talk to a lawyer.

Section 6 relates to prohibits providing child porn to a minor.

Section 7 requires a depiction of an identifiable minor.

[0] https://www.law.cornell.edu/uscode/text/18/2252A

Re: Open Sourcing a Deep Learning Solution for Detecting NSFW Images

#118

Post by re »

the_af wrote: post #107
bahro wrote: post #12

I should update my sexy map finder: http://exclav.es/2016/05/20/sexy-maps/

Those are some hot map pictures! I don't understand how Google's algorithm can be misled into finding sexiness in those. I imagine it has something to do with skin tones or flesh colors, but then what about the high-contrast patchwork of green and brown fields Google finds "likely to contain adult content"? That's totally puzzling. The confusion with medical images is way more understandable. If you squint, you can a…

> I don't understand how Google's algorithm can be misled into finding sexiness in those.

I'm reminded of a paper for which the authors generated different pictures of static that fooled neural network image classifiers into confidently identifying them as different objects: https://arxiv.org/abs/1412.1897

Wired summary: https://www.wired.com/2015/01/simple-pictures-state-art-ai-s...

> Computer vision and human vision are nothing alike. And yet, since it increasingly relies on neural networks that teach themselves to see, we’re not sure precisely how computer vision differs from our own. As Jeff Clune, one of the researchers who conducted the study, puts it, when it comes to AI, “we can get the results without knowing how we’re getting those results.”

Re: Open Sourcing a Deep Learning Solution for Detecting NSFW Images

#119

Post by cortesoft »

Asooka wrote: post #44
daturkel wrote: post #36

Earlier quoted context omitted.

The training set is almost certainly composed of copyrighted material.

Interesting thought - doesn't every single porn producer now have a valid copyright claim on the trained network? I don't see how you can argue this isn't a derivative work based on the movies they produced.

It would not be considered derivative work, because what is produced is nothing like the original. There is nothing recognizable in the work produced for a court to rule on.

This is like saying the hash of the text of a book is derivative. If it were ruled that this is the case (that a hash is a derivative work) then suddenly every single number in existence is a derivative of every single other number (since there will always exist some function that will transform X into Y.)

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