TensorFlow AdultPic Authentor

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This repo contains code for running Not Suitable for Work (NSFW) classification deep neural network Caffe models.

Not suitable for work classifier
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.

Usage

• The network takes in an image and gives output a probability (score between 0-1) which can be used to filter not suitable for work images. Scores < 0.2 indicate that the image is likely to be safe with high probability. Scores > 0.8 indicate that the image is highly probable to be NSFW. Scores in middle range may be binned for different NSFW levels.
• Depending on the dataset, usecase and types of images, we advise developers to choose suitable thresholds. Due to difficult nature of problem, there will be errors, which depend on use-cases / definition / tolerance of NSFW. Ideally developers should create an evaluation set according to the definition of what is safe for their application, then fit a ROC curve to choose a suitable threshold if they are using the model as it is.
• Results can be improved by fine-tuning the model for your dataset/ use case / definition of NSFW. We do not provide any guarantees of accuracy of results. Please read the disclaimer below.
• Using human moderation for edge cases in combination with the machine learned solution will help improve performance.

Description of model
We trained the model on the dataset with NSFW images as positive and SFW(suitable for work) images as negative. These images were editorially labelled.

Please note that deeper networks, or networks with more filters can improve accuracy. We train the model using a thin residual network architecture, since it provides good tradeoff in terms of accuracy, and the model is light-weight in terms of runtime (or flops) and memory (or number of parameters).