---
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