Model Details
- Architecture: [Briefly describe the model, e.g., ResNet50 backbone fine-tuned on an NSFW dataset, combined with a Temporal Segment Network.]
- Training Data: The model was trained on a proprietary dataset of [Number] curated videos/images, balanced across safe and unsafe categories.
Limitations & Ethical Considerations
While this AI model is a powerful tool for content moderation, it is not perfect. Please keep the following in mind:
- False Positives/Negatives: The model may occasionally flag safe content (e.g., medical procedures, classical art, breastfeeding) as NSFW, or miss highly obfuscated explicit content. Human-in-the-loop review is strongly recommended for edge cases.
- Context Blindness: AI models struggle with nuanced context. Satire, educational content, or news reporting may trigger the filter.
- Privacy: If you are processing user-uploaded videos, ensure you comply with relevant data privacy laws (GDPR, CCPA) and do not store sensitive user data without consent.
- Intended Use: This tool is intended for content moderation and safety. It should not be used to surveil individuals without consent.
Contributing
Contributions are welcome! If you have suggestions for improving the model, optimizing the pipeline, or expanding the dataset, please open an issue or submit a pull request.
License
ThisHere is a comprehensive README template for your NSFW video detection AI model. You can customize the bracketed information [like this] to match your project's specific details.
Video NSFW Detection AI 🛡️
Overview
This repository contains a machine learning pipeline designed to automatically detect Not Safe For Work (NSFW) content in video files. Built to assist in content moderation, trust and safety operations, and automated filtering, this model analyzes video streams to identify explicit material, including nudity, sexual content, and [insert other categories, e.g., extreme violence/gore].
Features
- Frame-by-Frame Analysis: Extracts and evaluates keyframes from video files to ensure high accuracy without processing redundant frames.
- Temporal Context (Optional): Uses [mention architecture, e.g., 3D CNNs / LSTMs / Vision Transformers] to analyze sequences of frames, reducing false positives by understanding context.
- Configurable Sensitivity: Adjustable confidence thresholds allow you to tune the model for either high recall (strict filtering) or high precision (fewer false alarms).
- Multi-Format Support: Compatible with standard video formats (MP4, AVI, MKV, MOV) via integration with [e.g., FFmpeg / OpenCV].
- Detailed Reporting: Outputs timestamped JSON reports indicating exactly when and where NSFW content appears in the video.
Table of Contents
Prerequisites
- Python 3.8+
- [PyTorch / TensorFlow] >= 2.0
- FFmpeg (for video processing)
- CUDA-compatible GPU (Highly recommended for faster inference)