Datasets:
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language:
- en
license: cc-by-nc-4.0
task_categories:
- image-classification
pretty_name: VPD-100K
size_categories:
- 100K<n<1M
tags:
- privacy
- computer-vision
- object-detection
- livestream
- visual-privacy
- icml-2026
- video privacy
paperswithcode_id: null
---
# VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection
> **Official dataset for the ICML 2026 paper**
>
> **VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection**
🌐 **Project Page:** https://vpd-100k.github.io/
📄 **Paper:** https://arxiv.org/abs/2605.10229
---
# Overview
Visual privacy protection has become increasingly important as people continuously share images and live-stream videos online. Existing visual privacy datasets are generally limited in scale, annotation granularity, and scene diversity, making it difficult to train models that generalize to real-world privacy-sensitive scenarios.
VPD-100K is a large-scale benchmark specifically designed for **generalizable visual privacy detection**. It contains **100,000 images**, over **190,000 annotated privacy instances**, and **33 fine-grained privacy categories** covering a broad spectrum of real-world privacy leakage scenarios.
The dataset was introduced in the following paper:
> **VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection**
> ICML 2026
---
# Highlights
- 📷 **100,000 images**
- 🎯 **190,000+ annotated privacy instances**
- 🏷️ **33 fine-grained categories**
- 🌍 Covers diverse real-world environments
- 📺 Designed for both image understanding and live-stream privacy protection
- 🔍 High-resolution images (over half exceed 1080p) for detecting tiny privacy-sensitive objects
---
# Privacy Taxonomy
VPD-100K organizes privacy-sensitive content into **four primary domains**.
## 1. Human Presence
Sensitive human identity information, including different types of faces under diverse environments.
Examples include:
- Adult faces
- Child faces
- Crowd faces
- Partial faces
---
## 2. On-Screen Personally Identifiable Information (PII)
Digital information displayed on monitors, phones, tablets, or other screens.
Examples include:
- Passwords
- Chat messages
- Email addresses
- User accounts
- Verification codes
- Banking interfaces
---
## 3. Physical Identifiers
Physical documents and objects that contain sensitive personal information.
Examples include:
- Passport
- ID card
- Bank card
- Boarding pass
- Ticket
- Driver license
---
## 4. Location Indicators
Objects revealing the physical location of users.
Examples include:
- Street signs
- Shop signs
- Community names
- Building names
- Address plates
---
# Dataset Statistics
| Property | Value |
|-----------|------:|
| Images | 100,000 |
| Object Instances | 190,000+ |
| Categories | 33 |
| Primary Domains | 4 |
| Resolution | Over 50% >1080p |
The dataset exhibits realistic characteristics including:
- Long-tail class distribution
- Small-object dominance
- High visual complexity
- Diverse indoor and outdoor environments
These properties make VPD-100K particularly suitable for evaluating privacy detection methods in challenging real-world applications.
---
# Intended Uses
VPD-100K can be used for:
- Visual privacy detection
- Object detection
- Privacy-aware computer vision
- Live-stream privacy protection
- Privacy-preserving AI
- Benchmarking privacy detection algorithms
---
# Ethical Considerations
To avoid exposing real users' sensitive information, privacy-critical scenarios involving digital interfaces (such as face and account information) are reconstructed using high-fidelity simulated environments instead of collecting real personal data whenever possible.
Researchers should ensure that models trained on this dataset are used responsibly and comply with applicable privacy regulations.
---
# Citation
If you use VPD-100K in your research, please cite:
```bibtex
@inproceedings{vpd100k2026,
title={VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection},
author={Hu, Xiaobin and Zuo, Enpu and Hu, Lanping and Yang, Kaiwen and Liao, Dianshu and Zhang, Tianyi and Yin, Bo and Zhou, Yinsi and Pan, Shidong and Sun, Xiaoyu},
booktitle={Proceedings of the International Conference on Machine Learning (ICML)},
year={2026}
}
```
---
# License
Please refer to the official project page for the latest licensing information.
---
# Links
- 🌐 Project Page: https://vpd-100k.github.io/
- 📄 Paper: https://arxiv.org/abs/2605.10229
- 🤗 Hugging Face Dataset: https://huggingface.co/datasets/XiaoyuSunANU/Visual_Privacy_Dataset |