XiaoyuSunANU/Visual_Privacy_Dataset

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

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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

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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

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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

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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

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3. Physical Identifiers

Physical documents and objects that contain sensitive personal information.

Examples include:

• Passport
• ID card
• Bank card
• Boarding pass
• Ticket
• Driver license

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4. Location Indicators

Objects revealing the physical location of users.

Examples include:

• Street signs
• Shop signs
• Community names
• Building names
• Address plates

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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.

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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

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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.

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Citation

If you use VPD-100K in your research, please cite:

@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}
}

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License

Please refer to the official project page for the latest licensing information.

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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