Not Safe for Work

An ML model for adult content detection


The NSFW machine-learning model is an advanced algorithm designed to accurately detect and classify adult content within digital media files. It plays a vital role in ensuring the safe and appropriate use of media assets in various industries.

By employing state-of-the-art computer vision techniques, the model offers powerful capabilities to automatically identify explicit and adult content, enhancing content moderation and compliance processes.

The model utilizes deep learning algorithms trained on extensive datasets to accurately identify and classify adult content, including nudity, explicit imagery and suggestive or provocative materials. It is seamlessly integrated into our service, allowing for efficient and scalable content analysis.

Use cases

Possible applications of adult content detection include:

  • Content moderation - The model can automatically filter and moderate user-generated content. It helps prevent the upload of adult or explicit materials, ensuring a safer online environment.

  • Brand protection - Advertisers can safeguard their reputations and protect their digital assets. By automatically screening media content before publishing or distribution, companies can maintain brand integrity and avoid association with inappropriate or NSFW materials.

  • Legal compliance - In industries such as publishing, advertising, and e-commerce, the NSFW model can help ensure compliance with legal and regulatory standards. By identifying and flagging adult content, companies can adhere to age restrictions, protect minors from explicit material and avoid legal repercussions.

  • Content Curation - Content creators can efficiently curate their collections. The automatic tagging and categorization process for assets makes it easier to search and manage content based on its appropriateness for different target audiences.

API endpoints

An up-to-date reference with all API endpoints is available here:

Example API responses

Input imageAPI response

Last updated