Working notes from the Scandinavian Institute for Computational Vandalism

NSFW “dreams”

In September 2016, engineers at Yahoo released a machine learning model trained to detect “NSFW” images. The acronym NSFW, “not safe for work”, is net slang for images with typically nudity or graphic sexual content. Recently (October 2016) PHD research Gabriel Goh has released experiments applying a means of generating images from such models.

https://github.com/yahoo/open_nsfw
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Detecting offensive / adult images is an important problem which researchers have tackled for decades. With the evolution of computer vision and deep learning the algorithms have matured and we are now able to classify an image as not suitable for work with greater precision.

Defining NSFW material is subjective and the task of identifying these images is non-trivial. Moreover, what may be objectionable in one context can be suitable in another. For this reason, the model we describe below focuses only on one type of NSFW content: pornographic images. The identification of NSFW sketches, cartoons, text, images of graphic violence, or other types of unsuitable content is not addressed with this model.

Since images and user generated content dominate the internet today, filtering nudity and other not suitable for work images becomes an important problem. In this repository we opensource a Caffe deep neural network for preliminary filtering of NSFW images.

https://open_nsfw.gitlab.io/

What makes an image NSFW, according to Yahoo? I explore this question with a clever new visualization technique by Nguyen et al.. Like Google’s Deep Dream, this visualization trick works by maximally activating certain neurons of the classifier. Unlike deep dream, we optimize these activations by performing descent on a parameterization of the manifold of natural images. This parametrization takes the form of a Generative Network, trained adversarially on an unrelated dataset of natural images.



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