VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection
2026-05-11 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputers and Society
AI summaryⓘ
The authors created a large and detailed image dataset called VPD-100K to help improve detecting sensitive private information in photos and videos. Their dataset includes 100,000 images labeled with many fine categories like people, ID cards, or locations, covering tricky real-world details. They also developed a new lightweight method using frequency information to better spot subtle private details beyond simple pixel checks. Tests showed that their dataset and method work well on both images and live video streams, helping prevent accidental privacy leaks.
Visual PrivacyDatasetPrivacy DetectionPersonally Identifiable Information (PII)Frequency-domain AttentionSpectral GatingLong-tailed DistributionObject AnnotationLive Streaming PrivacyImage Analysis
Authors
Xiaobin Hu, Enpu Zuo, Lanping Hu, Kaiwen Yang, Dianshu Liao, Tianyi Zhang, Bo Yin, Yinsi Zhou, Shidong Pan, Xiaoyu Sun
Abstract
Privacy protection has become a critical requirement in the era of ubiquitous visual data sharing, imposing higher demands on efficient and robust privacy detection algorithms. However, current robust detection models are severely hindered by the lack of comprehensive datasets. Existing privacy-oriented datasets often suffer from limited scale, coarse-grained annotations, and narrow domain coverage, failing to capture the intricate details of sensitive information in realworld environments. To bridge this gap, we present a large-scale, fine-grained Visual Privacy Dataset (VPD-100K), designed to facilitate generalized privacy detection. We establish a holistic taxonomy comprising four primary domains: Human Presence, On-Screen Personally Identifiable Information (PII), Physical Identifiers, and Location Indicators, containing 100,000 images annotated with 33 fine-grained classes and over 190,000 object instances. Statistical analysis reveals that our dataset features long-tailed distributions, small object scales, and high visual complexity. These characteristics make the dataset particularly valuable for demanding, unconstrained applications such as live streaming, where actors frequently face unintentional, realtime information leakage. Furthermore, we design an effective frequency-enhanced lightweight module consisting of frequency-domain attention fusion and adaptive spectral gating mechanism that breaks the limitations of spatial pixel intensity to better capture the subtle details of sensitive information. Extensive experiments conducted on both diverse image and streaming videos benchmarks consistently demonstrate the effectiveness of our VPD-100K dataset and the wellcurated frequency mechanism. The code and dataset are available at https://vpd-100k.github.io/.