IntroductionProblematic online video pornography consumption is associated with sexual objectification, particularly in male consumers. However, previous studies have not considered that there is a subgroup of internet users whose consumption may become problematic due to their internet sex addiction. Such users may, in response to internet sex addiction symptoms such as craving, have increased levels of sexual objectification.MethodsIn a sample of 1,272 male consumers of online video pornography (Mage = 32.93, SDage = 9.44), we examined whether internet sex addiction is linked to sexual objectification via an online survey.ResultsWe fitted a series of structural equation models and found that men who scored higher on internet sex addiction were more likely to objectify women. More importantly, this link did not cease when controlling for the frequency of online video pornography consumption.DiscussionOur findings suggest that there are other mechanisms related to addictive symptomatology than just the link through online video pornography consumption that may contribute to sexual objectification. Addiction-related factors may have a unique role in fostering sexual objectification. Isolating internet sex addiction as a potential driver highlights the need to address objectifying behaviours in individuals struggling with this addiction.

Context

This dataset is meant to aid development of effective and computationally light NSFW filtering that can be run on low powered devices. To understand why I'm posting this dataset, see this article.

NSFW machine learning requires NSFW images, which are best not distributed on public sites (and usually against Terms of Service). Instead, this dataset contains the model outputs of 200K mostly pornographic images having been sent through the first layers of MobileNetV2. Additionally, the output of the Yahoo NSFW model are included.

Transfer learning principles can then be applied to this dataset. Using the MobileNetV2 outputs as bottlenecks, and the Yahoo NSFW outputs as target values, one can build a model which tries to mimic the Yahoo NSFW model.

Content

The files are in several archives (it was the only way to upload this much data with a 2GB limit per file). Inside the archives are npz files (compressed numpy arrays), containing 2000 input and target tensors.

Keras was used to create the MobileNetV2 output, and you can see in the tutorial kernel how it can be utilized.