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Sit back grab some popcorn. Lets see how long it takes people to start running data backwards to get new original porn.
I see you never have seen what kind of images that tends to generate. Or you're really into R'lyehian porn.
I'm not a deep learning person whatsoever, but I do have an interesting use case that I won't disclose publicly: Is there a way to build this, and output detections based on the, ugh, object it has detected? e.g. penis 0.94 vagina 0.01
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.
Is the training set itself large, or are you meaning a... large training set?
(sorry I couldn't help myself)
Earlier quoted context omitted.
They actually fixed this with the 2003 PROTECT act which only makes obscene simulated child pornography illegal. Because obscenity isn't protected by the first amendment this has been found to be constitutional.
It's hard to say that changed anything. Obscenity is a notoriously thorny subject in American constitutional law. The legal test for determining obscenity[0] is highly subjective and in the context of the internet very difficult to apply. [0] https://en.wikipedia.org/wiki/Miller_test
The test:
* Whether "the average person, applying contemporary community standards", would find that the work, taken as a whole, appeals to the prurient interest,
* Whether the work depicts or describes, in a patently offensive way, sexual conduct or excretory functions specifically defined by applicable state law,
* Whether the work, taken as a whole, lacks serious literary, artistic, political, or scientific value.
Also:
Critics of obscenity law argue that defining what is obscene is paradoxical, arbitrary, and subjective.
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I think it would be hard to not find generated child porn obscene by this test, unless you have a good lawyer, at which point there is plenty of wiggle room.
Anyone else see the irony in acknowledging that NFSW is subjective and contextual, but assuming that pornographic images are not?
[1] Almost, because porn is SFW when your work involves porn.
Earlier quoted context omitted.
When you said "reversed" I thought about it being a porn-generative neural network. Just enter your favorite keywords and an unique, tailored to your needs scene will be generated just for you!
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 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.
Anyone else see the irony in acknowledging that NFSW is subjective and contextual, but assuming that pornographic images are not?
For instance, if you read it like this, it still makes sense (I replaced NSFW with pornographic):
Disclaimer: The definition of pornographic is subjective and contextual. This model is a general purpose reference model, which can be used for the preliminary filtering of pornographic images. We do not provide guarantees of accuracy of output, rather we make this available for developers to explore and enhance as an open source project.
So can it be reversed to become the ultimate porn-finding neural network?