
What is the best way to programatically detect porn images? (2009) - romain_g
http://stackoverflow.com/questions/713247/what-is-the-best-way-to-programatically-detect-porn-images/713414
======
nailer
I used pifilter (now WeSEE:Filter ,
[http://corp.wesee.com/products/filter/](http://corp.wesee.com/products/filter/))
for a production, realtime, anonymous confession site (imeveryone.com) in
2010.

It cost, IIRC, a tenth of a cent per image URL. Rather than being based on
skin tone, it was created based on algos to specifically identify labia,
anuses, penises, etc. REST API: send a URL, get back a yes/no/maybe. You
decided what to do with the maybes.

My experience:

\- Before launch, I tested it with 4chan b as a feed, and was able to produce
a mostly clean version of b with the exception of cartoon imagery.

\- It could catch most of the stuff people tried to post to the site. Small
breasted women (being that breasts are considered 'adult' in the US) was the
only thing that would get through and wasn't a huge concern. Completely
unmaintained public hair (as revealing as a black bikini) would also get
through.

\- Since people didn't know what I was testing with they didn't work around it
(so nobody tried posting drawings or cartoons), but I imagine eg a photo of a
prolapse might not trigger the anus detection as the shape would be too
different.

\- pifilter erred on the side of false negative, but one notable false
positive: a pastrami sandwich.

~~~
cobrausn
I don't remember where I read this, but someone once recommended having a bot
that posts the image to 4chan b feed and monitor the views and replies. Since
porn images on b usually had somewhat predictable replies, it would be a good
way to augment another technique and prevent false positives. Funny idea, but
not really sure how viable.

~~~
t2d2
Using /b/ as an unpaid mechanical Turk alternative sounds intriguing yet
profoundly disturbing.

Imagine being able to fire off a dozen /b/tards bots at the website or target
of your choice.

------
Theodores
In The Olden Pre-Digital Days porn was either in print or on a television
screen. Back then (we are talking two whole decades ago) experienced broadcast
engineers could instantly spot porn just by catching a look at an oscilloscope
(of which there were usually many in a machine room).

Notionally the oscilloscope would be there to show that the luminance and
chroma was okay in the signal (i.e. it could be broadcast over the airwaves to
look as intended at the other end - PAL/NTSC), however, porn and anything
likely to be porn had a distinctive pattern on the oscilloscope screen. Should
porn be suspected then the source material would obviously be patched through
to a monitor 'just in case'.

Note that the oscilloscope was analog and that the image would be changing
25/30 times a second. Also, back then there were not so many false positives
on broadcast TV, e.g. pop videos etc. where today's audience deems them artful
rather than porn.

If I had to solve the problem programatically I would find a retired broadcast
engineer and start from there, with what can be learned from a 'scope.

~~~
daurnimator
It's probably related to the type of cameras available in the day rather than
anything else...

------
adorable
I have developed an algorithm to detect such images, based on several articles
published by research teams all over the world (it's incredible to see how
many teams have tried to solve this problem!).

I found out that no single technique works great. If you want an efficient
algorithm, you probably have to blend different ideas and compute a "nudity
score" for each image. That's at least what I do.

I'd be happy to discuss how it works. Here are a few techniques used:

\- color recognition (as discussed in other comments)

\- haar-wavelets to detect specific shapes (that's what Facebook and others
use to detect faces for example)

\- texture recognition (skin and wood may have the same colors but not the
same texture)

\- shape/contour recognition (machine learning of course)

\- matching with a growing database of NSFW images

The algorithm is open for test here:
[http://sightengine.com](http://sightengine.com) It works OK right now but
once version 2 is out it should really be great.

------
asolove
Amazon Mechanical Turk has an adult-content marker specifically for this
purpose. Lots of people have done the paperwork to qualify for adult-content
jobs and the cost of having humans do it at scale is very low:
[https://requester.mturk.com/help/faq#can_explicit_offensive](https://requester.mturk.com/help/faq#can_explicit_offensive)

Source: I helped implement a MT job to filter adult content for a large
hosting company.

~~~
phorese
So i can literally get paid for looking at porn. Huh, who knew... :)

~~~
selmnoo
At the cost of your mental health, sure.

I recall reading an article about the human workers who had this job at
Google... they had crap benefits, crap pay, and no mental healthcare. As a
result a lot of the people in the field had depression and other mental
issues.

~~~
sharkweek
Friend was a contractor on the YouTube filter team -- they lasted about 9
months before it became too much and had to leave.

There was absolutely no reward in showing up to work, and some of the things
they saw have likely scarred their memories forever

~~~
differentView
Challenge accepted!

------
ma2rten
I did this for my bachelor thesis for a company that shall remain unnamed. I
am pretty confident that my approach works better than any of the answer
posted on stackoverflow.

I used the so called Bag of Visual Words approach. At that time the state of
the art in image recognition (now it's neural networks). You can read about on
Wikipeida. The only main change from the standard approach (SHIFT + k-means +
histograms + SVM + chi2 kernel) was that I used a version of SHIFT that uses
color features. In addition to this I used a second machine learning
classifier based on the context of the picture. Who posted it? Is it a new
user? What are the words in the title? How many view does the picture have....

In combination the two classifiers worked nearly flawless.

Shortly after that, chat roulette has having it's porn problem and it was in
the media that the founder was working on a porn filter. I send an email to
offer my help, but didn't get an reaction.

~~~
anjc
SHIFT or SIFT? What's SHIFT?

~~~
mturmon
I think he means SIFT. I'm not aware of SHIFT either, and a look at his PAMI
paper shows use of color features as well as various SIFT features.

------
VLM
This is probably going to get downvoted, but if lots of people are not
overzealous puritans and want some skin, the best overall system design that
maximizes happiness and profit is probably sharding into

puritanweirdos.example.com with no skin showing between toes and top of
turtleneck (edited to add no pokies either)

and

normalpeople.example.com with 99% of the human race

The best solution to a problem involving computers is sometimes computer
related, but sometimes is social. The puritans are never going to get along
with the normal people anyway, so its not like sharding them is going to hurt.

Another way to hack the system is not to hire or accept holier than thou
puritans. Personality doesn't mesh with the team, doesn't fit culture, etc.
You have to draw the line somewhere, and weirdos on either end should get cut,
so no CP or animals at one extreme, and no holy rollers on the other extreme.

The final social hack is its kind of like dealing with bullies via
appeasement. So they're blocking reasonable stuff today, tomorrow they want to
block all women not wearing burkhas or depictions of women damaging their
ovaries by driving. Appeasing bullies never really works in the long run, so
why bother starting. "If you claim not to like it, or at least enjoy telling
everyone else repeatedly how you claim not to like it, stop looking at it so
much, case closed"

~~~
Shivetya
Harassment laws/lawyers would find you their wet dream. The rule with pictures
in any work environment is to err on the side of extreme caution.

More than likely your not going to find out they don't mesh with the team till
they quit or are fired. If your lucky its not followed by a lawsuit.

See, its not their job to not be offended, it is your job to offer a
harassment free work environment. Whom you are catering too depends on what is
PC or not at the time, fortunately its pretty easy to determine whose whims to
cater to or not

~~~
Daniel_Newby
Nope. Harrassment is a legal term of art, not a synonym for disagreeable
treatment. Harrassment occurs when a person or group is singled out for
distinct treatment, in the U.S. at least. Subjecting everybody to all types of
porn is quite lawful. (There is a famous case of a bisexual pervert who
indiscriminantly propositioned anything with a pulse. His self-designated
victims were bitch slapped out of court.)

~~~
aestra
What? No.

Sexual harassment is _unwelcome_ sexual advances, requests for sexual favors,
and other verbal or physical harassment of a sexual nature, no matter how many
people are sexually harassed or the gender of the parties involved.

There have been class action sexual harassment lawsuits. Jenson v. Eveleth
Taconite Co. was the first which represented fifteen women.

[http://en.wikipedia.org/wiki/Jenson_v._Eveleth_Taconite_Co](http://en.wikipedia.org/wiki/Jenson_v._Eveleth_Taconite_Co).

It's a fuzzy ground with say prominent displays of pornographic material in
say an office environment. One might argue that it can make the workplace a
hostile or overtly sexually charged environment. It is best to err on the side
of caution.

~~~
VLM
Good lord I've read that case and its about as far from the pr0n filter
example as you can possibly get. That place was beyond nuts and so was the
trial. They deserved every penny they got because the workplace was out of
control crazy, and merely coincidentally happened to involve male/female
issues.

That is NOT a case about accidentally clicking on a web page or debating if
huffpo headline stories are sometimes a little too racy, that was a case about
a madhouse of stalking and intimidation and tire slashing and... I don't think
a mere web pr0n image filter would have saved the mine 3.5 million dollars.
Actually having capable supervisors and managers, yeah, that might have
worked?

~~~
aestra
I should have been more specific. The defense "I sexually harass everyone, so
it isn't sexual harassment" is just not in any way valid. Full stop. Period.

I used the first class action sexual harassment lawsuit as an example to
illustrate the point of "just because it's company culture/I do it to
everyone/he's just like that" doesn't make it not harassment.

The GP was giving false information that if you sexually harass everyone, than
it magically becomes not sexual harassment and then it somehow gets thrown out
in court, but does not cite the relevant court case. Just because you ass grab
everyone that walks by doesn't make ass grabbing not sexual harassment by the
law.

That's mostly my point.

As far as a p0rn filter, that's a different story. You can look at it a
different way, legally.

I'm just playing devil's advocate here as to why it would be a good idea for
someone to try to err on the side of caution in the workplace filter rules.

They may want to avoid the situation where person A is sitting in their cube
while person B is watching hardcore gay porn next door specifically to harass
person A. Person A might feel that the employee didn't do a good enough job to
prevent this type of harassment by blocking access to that content.

If the employer doesn't try to filter out porn or at least create a policy
against it, it might look like it is condoning or supporting it whereas their
peers filter. (I just made that up, if say 85% of their peers make an attempt
to filter) especially if it is well known that managers tolerate risque
images/video if not outright porn, or if company culture tolerates it.

A less grey example is if you put a picture of two women who are mostly
clothed but kissing and put it in the office breakroom. Everyone sees it, but
it creates a sexually charged environment that can be hostile to some. That's
a big no-no in HR land.

~~~
VLM
One important point to make about Taconite and your examples are I cannot
comprehend how any of it could remotely relate to work; management needs to
write them up and fire them not because the victims were women or it somehow
had something to do with sex but because the workplace is apparently
completely out of control. This isn't some pie in the sky daydream, this is
decades of observation at a variety of F500 companies in the midwest.

The problem with some jerk harassing female coworkers with .. full stop ...
harassing coworkers comma, is he's not doing his job, and he's preventing
others from doing their job, and the only mystery is why that problem is not
being taken care of at that level. Its not pass the buck time, why didn't some
dude in another department find some hairbrained technical scheme to try to
temporarily stop the nutcase from bothering people... he's got a boss who's
already responsible for that task.

Is this one of those weird coastie vs heartland type issues where we have real
bosses so coastie stories sound weird, or ...

That's the crazy part of the story. If the boss simply doesn't care, then
blocking certain pix on port 80 is merely going to result in your example of
hanging up pictures, or carrying in zip drives, or whatever, obviously the
boss won't care about those either. So the net result is a huge waste of time
and money for ... nothing?

I would think there's some CYA going on if you know there's about to be
another 3.5 million dollar "taconite" type lawsuit filed, and its going to be
a slam dunk, whoever it is ordering hairbrained technical solutions doomed to
failure probably won't be punished as hard as the guy doing the harassment or
his boss who doesn't care, or his bosses boss. But a legal hit like that means
tune up the resume anyway, its not looking good.

------
_mulder_
Here's an idea...

Develop a bot to trawl NSFW sites and hash each image (combined with the 'skin
detecting' algorithms detailed previously). Then compare the user uploaded
image hash with those in the NSFW database.

This technique relies on the assumption that NSFW images that are spammed onto
social media sites will use images that already exist on NSFW sites (or are
very similar to). Then it simply becomes a case of pattern recognition, much
like SoundHound for audio, or Google Image search.

It wouldn't reliably detect 'original' NSFW material, but given enough cock
shots as source material, it could probably find a common pattern over time.

edit: I've just noticed rfusca in the OP suggests a similar method

~~~
pa5tabear
How do you program that sort of thing?

Do you have to tell it what shapes/colors to look for? Or do a combination of
overall image similar combined with localized image similarity and portion by
portion image comparison?

~~~
viraptor
Maybe recognising the furniture in the background would work too ;) I remember
there was a website/catalog of IKEA furniture somewhere made using NSFW
photos.

------
mixmax
detecting all porn seems to be an almost impossible problem. Many kinds of
advanced porn (BDSM, etc.) don't have much skin - often the actors are in
latex, tied up, or whatever. It's obviously porn when you see it, but
detecting it seems incredibly hard.

Detecting smurf-porn(1) (yes that's a thing...) is even harder since all the
actors are blue.

[http://pinporngifs.blogspot.dk/2012/09/smurfs-
porn.html?zx=7...](http://pinporngifs.blogspot.dk/2012/09/smurfs-
porn.html?zx=7ac31f5871d5a788) \- obviously very NSFW, but quite funny.

~~~
ma2rten
It is possible high accuracy if you use machine learning and a sufficiently
large training set. That said said even humans sometimes don't agree is
something is porn or not.

~~~
rfergie
Just saying "machine learning" is not very useful here. What machine learning
techniques work well in this case and what are the major pitfalls?

Then you can convince me that a "sufficiently large training set" exists and
is smaller than "all the images on the internet".

~~~
ma2rten
See my other post on this page.

I would argue that this less difficult than you average image classification
problem. Just have a look at what kind of challenges image classification can
tackle, picking the correct class out of 1000s of classes. Porn is normally
well-lit, the subject is at the center of the image, etc.

The main difficulty is to define what is porn and what is not ... It's easy
see the difference between porn and pictures of bicycles. But how about porn
and artistic nudity? You see it's actually a scale, but you are trying to make
a binary decision.

Another problem (at least with the method I explained below) is that portraits
sometimes get misclassified. Maybe it could help to integrate face detection.
I'd suspect that more recent models would not have this problem (e.g. ones
that take not only local features into account). Other times it makes
mistakes, where you think "why on earth would think this is porn". Again
combing different method should help to eliminate those outliers.

Outliers are also a problem, e.g. black and white pictures. Again an ensemble
of different models (e.g. one color independent one) might help. Niches are
not really a big problem. BSDM porn is as far as I have seen the only niche of
porn that really different visually.

------
eksith
To this day, I believe the best method for picking out these images is a human
censor (with appropriate, company provided, counseling afterward).

Edit: No shortage of stock image reviewer jobs
[https://google.com/search?hl=en&q=%22image%20reviewer%22](https://google.com/search?hl=en&q=%22image%20reviewer%22)

I'm trying to find an interview of one of these people describing what it's
like on the other end. It wasn't a pleasant story. These folks are employed by
the likes of Facebook, Photobucket etc... Most are outsourced, obviously, and
they all have very high turnover.

~~~
RossM
I seem to remember reading an article about people doing this at Google(?) in
a pretty poor state.

Edit: I think it was this one: [http://www.buzzfeed.com/reyhan/tech-
confessional-the-googler...](http://www.buzzfeed.com/reyhan/tech-confessional-
the-googler-who-looks-at-the-wo)

~~~
eksith
Yep! That's the one. Thanks.

A pretty unpleasant job no matter what angle you look at it.

I remember this bit the most : "Google covered one session with a government-
appointed therapist — and encouraged me to go out and get my own therapy after
I left."

This is the down side to 'abstracting away' the dirty end of filtering. I'm
looking forward to a day when this can be properly automated, but, considering
the ever-changing nature of erotica to begin with, I don't see that happening
any time soon.

~~~
XorNot
Developing a strong AI which can do this without going insane itself is going
to be the robo-pysche challenge of the future.

------
VLM
Nobody has discussed i18n and l10n issues? What passes for pr0n in SF is a bit
different than tx.us and thats different from .eu and from .sa (sa is saudi
arabia not south africa, although they've probably got some interesting
cultural norms too)

If you're trying for "must not offend any human being on the planet" then
you've got an AI problem that exceeds even my own human intelligence problem
to figure out. Especially when it extends past pr0n and into stuff like
satire, is that just some dudes weird self portrait, or a satire of the
prophet, and are you qualified to figure it out?

------
betterunix
How about a picture of a woman's breasts? What about an erect penis? Sounds
like porn, but you might also see these things in the context of health-
related pictures or some other educational material.

The classic problem of trying to filter pornography is trying to separate it
from information about human bodies. I suspect that doing this with images
will be even harder than doing it with text.

~~~
Zimahl
Definitely true. Facebook had a dust-up when a woman posted a topless photo of
herself after she had had a double mastectomy.

That said, not all sites are like Facebook and we aren't talking about
filtering all the images on the internet, just ones on specific sites. One
example I can think of is that a forum for a sports team might not want NSFW
pictures posted as it would be irrelevant.

------
quarterto
Google reverse image search can come up with a search likely to return the
given image. Perhaps this can be used for porn classification.

------
nathanb
Seems like we were having this same problem with email spam, and Bayesian-
based learning filters revolutionized the spam filtering landscape. Has anyone
tried throwing computer learning at this problem?

We as humans can readily classify images into three vague categories: clean,
questionable, and pornographic. The problem of classification is not only one
of determining which bucket an image falls into but also one of determining
where the boundaries between buckets are. Is a topless woman pornographic? A
topless man? A painting of a topless woman created centuries ago by a well-
recognized artist? A painting of a topless woman done yesterday by a
relatively unknown artist? An infant being bathed? A woman breastfeeding her
baby? Reasonable people may disagree on which bucket these examples fall in.

So what if I create three filter sets: restrictive, moderate, and permissive,
and then categorize 1,000 sample images as one of those three categories for
each filter set (restrictive could be equal to moderate but filter
questionable images as well as pornographic ones).

Assuming that the learning algorithm was programmed to look at a sufficiently
large number of image attributes, this approach should easily be capable of
creating the most robust (and learning!) filter to date.

Has anyone done this?

~~~
clienthunter
This was my first thought. With a good training set and a savvy algo I believe
machine learning can be good with images, and theres an unprecedented amount
of training sets out there to be scraped...

------
Houshalter
Everyone is focusing on the machine vision problem but the OP had a good idea:

>There are already a few image based search engines as well as face
recognition stuff available so I am assuming it wouldn't be rocket science and
it could be done.

Just do a reverse image search for the image, see if it comes up on any porn
sites or is associated with porn words.

------
lectrick
Relevant:

[http://en.wikipedia.org/wiki/I_know_it_when_I_see_it](http://en.wikipedia.org/wiki/I_know_it_when_I_see_it)

Basically, it's impossible to completely accurately identify pornography
without a human actor in the mix, due to the subjectivity... and especially
considering that not all nudity is pornographic.

------
primaryobjects
This is a classic categorical problem for machine learning. I'm surprised so
many suggestions have involved formulating some sort of clever algorithm like
skin detection, colors, etc. You could certainly use one of those for a
baseline, but I'd bet machine learning would out-score most human-derived
algorithms.

Take a look at the scores for classifying dogs vs cats with 97% accuracy
[http://www.kaggle.com/c/dogs-vs-
cats/leaderboard](http://www.kaggle.com/c/dogs-vs-cats/leaderboard). You could
use a technique of digitizing the image pixels and feeding to a learning
algorithm, similar to
[http://www.primaryobjects.com/CMS/Article154.aspx](http://www.primaryobjects.com/CMS/Article154.aspx).

------
denzil_correa
I am aware of some nice scholarly work in this space. You may find Shih et al.
approach of particular interest [0]. Their approach is very straight forward
and based on image retrieval. They have also reported an accuracy of 99.54%
for Adult image detection in their dataset.

[0] Shih, J. L., Lee, C. H., & Yang, C. S. (2007). An adult image
identification system employing image retrieval technique. Pattern Recognition
Letters, 28(16), 2367-2374. Chicago

[http://sjl.csie.chu.edu.tw/sjl/albums/userpics/10001/An_adul...](http://sjl.csie.chu.edu.tw/sjl/albums/userpics/10001/An_adult_image_0identification_system_employing_image_retrieval_technique.pdf)

------
jmngomes
I came across nude.js ([http://www.patrick-
wied.at/static/nudejs/](http://www.patrick-wied.at/static/nudejs/)) when
researching for a social network project, seems quite nice and is Javascript
based.

------
racbart
Wouldn't testing for skin colors produce far too many false positives to be
useful? All these beach photos, fashion lingerie photos, even close portraits.
And how about half of music stars these days who seem to try to never get
caught more clothed than half naked?

Nudity != porn and certainly half-nudity != porn.

I'd rather go for pattern recognition. There's lot of image recognition
software these days that can distinguish the Eiffel Tower from the Statue of
Liberty and it might be useful to detect certain body parts and certain body
configurations (for these shots that don't contain any private body part but
there are two bodies in an unambiguous configuration).

~~~
betterunix
"detect certain body parts"

When I was a kid, we had a firewall at school that tried to filter pornography
by doing something similar with text. Doing research on breast cancer turned
out to be rather tricky.

So let's say you try to detect certain body parts. Now you have someone who
wants to know more about their body, but you are classifying images from
medical / health articles as pornography.

"certain body configurations"

So now instead of having trouble reading about my own body, I will have
trouble looking at certain martial arts photos:

[https://upload.wikimedia.org/wikipedia/commons/1/14/BostonKe...](https://upload.wikimedia.org/wikipedia/commons/1/14/BostonKetsugoJujutsu.jpg)

I am not saying these are unsolvable problems, but they are certainly _hard_
problems. Even using humans to filter images tends to result in outrageous
false positives sometimes:

[http://abcnews.go.com/Health/breastfeeding-advocates-hold-
fa...](http://abcnews.go.com/Health/breastfeeding-advocates-hold-facebook-
protest/story?id=15530012)

~~~
racbart
You are correct and I'm not saying that I described the Holy Grail of
detecting porn in a single paragraph. I'm just pointing to another direction.
No solution to a very complex problem could be one-dimensional. Combining
several different tests might lead to a solution. I.e. these jujitsu photos
should not even be detected as “certain body configurations” as people there
are fully clothed and there's not much actual bodies seen in the picture (so
mentioned skin color definitely should come to play when detecting wether you
see a body or not).

At the end of the day I doubt there could be a fully bulletproof and always
correct solution using current state of tech. But you need to factor much more
than just skin color if you try to build an automated solution to this
problem.

~~~
icebraining
There's plenty of fully clothed porn out there. They're even tags on some porn
sites.

------
hugofirth
Whilst I agree that programmatically eliminating porn images is a very hard
problem. Programmatically filtering porn websites might be easier, beyond just
a simple key word search and whitelist.

If you assume that porn tends to cluster, rather than exist in isolation, then
a crawl of other images on the source pages , applying computer vision
techniques, should allow you to block pages that score above a threshold
number of positive results (thus accounting for inaccuracy and false
positives).

~~~
VLM
"score above a threshold number of positive results"

How about social scoring? A normal (or even a weirdo) teenage boy would spend
less than a second examining my ugly old profile pix, but after ten or so of
your known teen male users are detected to spend 5 minutes at a time, a couple
times a day, closely studying a suspected profile pix, I think you can safely
conclude that pix is not a pix of me and then flag / censor / whatever it for
the next 10K users.

~~~
hugofirth
The graph theorist in me is rubbing my hands in glee at the thought of seeing
if you could extrapolate out that approach to catch a broader range of
offensive imagery through relationships and usage patterns.

~~~
XorNot
Key problem: profile pictures are _everywhere_ on a website by definition.
People look at them for over an hour at a time depending how your site is laid
out.

If you wanted to spot shock imagery it's easier - study navigation aways or
rapid scrolling.

------
ismaelc
You can use APIs like these to do nude detection -
[https://www.mashape.com/search?query=nude](https://www.mashape.com/search?query=nude)

------
unoti
If you're interested in Machine Learning, the outstanding Coursera course on
machine learning just started a couple of days ago. It covers a variety of
machine learning topics, including image recognition. The first assignment
isn't due for a couple of weeks, so it's a perfect time to jump in and take
the machine learning course!

[https://www.coursera.org/course/ml](https://www.coursera.org/course/ml)

------
beat
Algorithmic solutions will always be hard. "I know it when I see it" is hard
to program.

Depending on the site, I'd go to a trust-based solution. New users get their
images approved by a human censor (pr0n == spambot in most cases). Established
users can add images without approval.

If you're going to try software, try something that errs on the side of
caution, and send everything to a human for final decision-making, just like
spam filters.

------
npatten
"You can programatically detect skin tones - and porn images tend to have a
lot of skin. This will create false positives but if this is a problem you can
pass images so detected through actual moderation. This not only greatly
reduces the the work for moderators but also gives you lots of free porn. It's
win-win."

hilarious!

------
hcarvalhoalves
Pornography is so creative that I find it hard to have one algorithm that can
detect it all. Looking for features certainly wouldn't catch the more weird
stuff.

Maybe a good approach is an image lookup, trying to find the image on the web
and seeing if it appears on a porn site, or a pornographic context.

------
jcfiala
It seems to me that if you could somehow solicit comments on the picture, you
then could do text analysis on the comments to see if someone thought they
were porn or not. (Well, I'm being a little silly, but there's a germ of an
idea there.)

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nate510
A corollary of Rule 34 is that an algorithm to classify porn is NP-Hard.

Um, so to speak.

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singlow
So, who's going to write the ROT13 algorithm for images. Just call it ROT128
and rotate the color value of the bits and use a ROT128 image viewer to view
the original image.

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kalleboo
That function is typically called "Invert".

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wehadfun
Probably the easiest way is with motion and sound. Checking for skin would be
hard depending on the type of content as mixmax pointed out

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djent
It wouldn't solve the entire problem, but you could look for the watermarks
that major porn networks stamp on their images.

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dschiptsov
by filename ,)

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bedhead
Maybe we can channel Potter Stweart into an algorithm somehow?

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talleyrand
That's what I was thinking, counselor.

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level09
would any one be interested in purchasing an API subscription for this kind of
service ? IMO, a pipeline of AI filters can be efficient to some extent.

~~~
ismaelc
Put it in Mashape :) (Disclaimer: I work for Mashape)

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digitalsushi
Invent an algorithm that can calculate humanity's creative thoughts.

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bicknergseng
Use CrowdFlower.

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bachback
machine learning? because you also want to filter cats.

~~~
rspeer
Machine learning is a field of research, not a magical incantation that you
say to solve everything.

