image-nsfw-detector¶

• Main author - HEIA-FR
• Code
• Deployment configuration
• Staging
• Production (not available yet)

Description¶

This service takes as input an image and returns a json with information about the probability that it includes NSFW content.

NSFW content detection¶

NSFW stands for not safe for work. This Internet slang is a general term associated to un-appropriate content such as nudity, pornography etc. See e.g. https://en.wikipedia.org/wiki/Not_safe_for _work. It is important to exercise caution when viewing or sharing NSFW images, as they may violate workplace policies or community guidelines.

The current service encapsulates a trained AI model to detect NSFW images with a focus on sexual content. Caution: the current version of the service is not able to detect profanity and violence for now.

Definition of categories¶

The border between categories is sometimes thin, e.g. what can be considered as acceptable nudity in some cultural context would be considered as pornography by others. Therefore we need to disclaim any complaints that would be done by using the model trained in this project. We can't be taken responsible of any offense or classifications that would be falsely considered as appropriate or not. To make the task even more interesting, we went here for two main categories nsfw and safe in which we have sub-categories.

• nsfw:
• porn: male erection, open legs, touching breast or genital parts, intercourse, blowjob, etc; men or women nude and with open legs fall into this category; nudity with sperma on body parts is considered porn
• nudity: penis visible, female breast visible, vagina visible in normal position (i.e. standing or sitting but not open leg)
• suggestive: images including people or objects making someone think of sex and sexual relationships; genital parts are not visible otherwise the image should be in the porn or nudity category; dressed people kissing and or touching fall into this category; people undressing; licking fingers; woman with tong with sexy bra
• cartoon_sex: cartoon images that are showing or strongly suggesting sexual situation
• safe:
• neutral: all kind of images with or without people not falling into porn, nudity or suggestive category
• cartoon_neutral: cartoon images that are not showing or suggesting sexual situation

Inspecting the output giving probabilities for the categories (safe vs not-safe) and the sub-categories, the user can decide where to place the threshold on what is acceptable or not for a given service.

Data set used to build the model¶

A dataset was assembled using existing NSFW image sets and was completed with web scraping data. The dataset is available for research purpose - contact us if you want to have an access. Here are some statistics about its conent (numbers indicate amount of images). The dataset is balanced among the categories, which should avoid biased classifications.

Model training and performance¶

We used transfer learning on MobileNetV2 which present a good trade-off between performance and runtime efficiency.

In this Table, the performance is reported as accuracy on the safe vs not-safe (sa/ns) main categories and on the sub-categories (sub). The sub performance in indeed lower as we have naturally more confusion between some categories and as there is simply a larger cardinality in the number of classes.

The API documentation is automatically generated by FastAPI using the OpenAPI standard. A user friendly interface provided by Swagger is available under the /docs route, where the endpoints of the service are described.

This service only has one route /compute that takes an image as input, which will be used to detect NSFW content.

Environment variables¶

Check the Core concepts > Service > Environment variables documentation for more details.

Run the tests with Python¶

Check the Core concepts > Service > Run the tests with Python documentation for more details.

Start the service locally¶

Check the Core concepts > Service > Start the service locally documentation for more details.