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... - obviously very NSFW, but quite funny.
Then you can convince me that a "sufficiently large training set" exists and is smaller than "all the images on the internet".
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.
man 1 smurf-porn ?!?!