Porn has been proven to have severe detrimental physical and mental effects, including: erectile dysfunction, depression, changing perceptions of sex, etc.
Feel free to google them (going on google scholar and typing in “pornography negative impacts” is a good start), or I can cite a laundry list of studies saying the same thing.
I'm unfamiliar with 'pornography recognition' as an established task in ML research (lol), but for what it's worth, it's not an innovate use of CNNs for audio classification. You can essentially turn any audio classification task into an image problem (raw audio into features like spectrograms/MFCCs). Which people have been doing since forever (by which I mean a number of years now).
The question of it in my mind is if the available features are structurally adequate.
Porn videos frequently have a soundtrack with distinctive moaning and that's one thing. If the problem was distinguishing audio recordings of somebody talking about erotic or not-erotic subjects, that's something entirely different.
Whether you like porn or hate porn, classifying porn is useful. There is a lot of it around and it causes a lot of practical problems if you are hosting public content if you're not in control of it.
It's like telling me you don't have kids without telling me you don't have kids xD.
I have my 8 year old sons internet pretty much locked down at this point, but people still end up skirting around the rules of sites like youtube when publishing content towards kids. Automatic filtering is one of the only ways to prevent newly published content geared towards children from being malicious. The alternative, human curation, even if done by method of reporting a video, is time consuming and expensive.
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[ 2.4 ms ] story [ 31.3 ms ] threadFirst sentence. This is either a great humanitarian scouring for the victims of child porn. Or it is a useless puritanical crusade against porn.
I started reading the paper and this is just about porn filtering, pretty much useless perhaps even harmful.
I am sure using CNNs to do this is innovative, but if you are going to start from a lie why should I trust your research?
Feel free to google them (going on google scholar and typing in “pornography negative impacts” is a good start), or I can cite a laundry list of studies saying the same thing.
Porn videos frequently have a soundtrack with distinctive moaning and that's one thing. If the problem was distinguishing audio recordings of somebody talking about erotic or not-erotic subjects, that's something entirely different.
Exactly my thought process. Maybe I should learn modern ML and apply my, ahem expertise, and apply for some grants.
It's like telling me you don't have kids without telling me you don't have kids xD.
I have my 8 year old sons internet pretty much locked down at this point, but people still end up skirting around the rules of sites like youtube when publishing content towards kids. Automatic filtering is one of the only ways to prevent newly published content geared towards children from being malicious. The alternative, human curation, even if done by method of reporting a video, is time consuming and expensive.