No. I looked into this a lot for a dating app I ran and no algorithms came close to human moderation, even for images you'd consider are obviously pornographic, which can get expensive.

A funny idea I had was to reverse the whole system - feed UGC content of a site that's supposed to be SFW into a porn site which is definitely NSFW, one that has lots of thumbnails. The ones that don't get any clicks to enlarge probably aren't porn and can pass the test :)

http://davidwalsh.name/nudejs http://popscan.blogspot.nl/2012/08/skin-detection-in-digital... https://www.pyimagesearch.com/2014/08/18/skin-detection-step... etc etc

ML is very difficult for porn. You end up with so many false positives it almost becomes useless compared to human moderation. "Porn" is also a very context sensitive term. What is porn vs. nude art? Is it still porn if the people in it aren't "pink"? What about cultural differences? What is considered inappropriate in the US may not be inappropriate in Europe, etc. How much skin has to be shown for it to be porn? There are so many questions. I'm sure detecting porn accurately will be one of the hardest problems we'll overcome in computer vision, as outrageous as that sounds - because of the level of context that is required.

See also: SFW porn - where other images are super imposed on top of real porn. It's hilarious, but is it really SFW? You decide. http://www.reddit.com/r/sfwporn

ML is very limited. It tagged African-American people as Gorillas for example. White people it tagged as dogs or goats. The African-american people were more outraged because being called a gorilla is racist. Even if the ML doesn't know what race is. Facial recognition is also hard, the ML confuses some people for others who have similar faces but are different people. ML just isn't at the level where it is reliable enough to do what we ask of it yet.