# SHAN: Object-Level Privacy Detection via Inference on Scene Heterogeneous Graph

arXiv: https://arxiv.org/abs/2403.09172 · HTML: https://mlsanity.com/paper/2403.09172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 40 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 30 of 30)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Scene Graph Generation by Iterative Message Passing](https://mlsanity.com/paper/1701.02426/) (2017)
- [Neural Motifs: Scene Graph Parsing with Global Context](https://mlsanity.com/paper/1711.06640/) (2017)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [Learning to Compose Dynamic Tree Structures for Visual Contexts](https://mlsanity.com/paper/1812.01880/) (2018)
- [Heterogeneous Information Network Embedding for Recommendation](https://mlsanity.com/paper/1711.10730/) (2017)
- [A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources](https://mlsanity.com/paper/2011.14867/) (2020)
- [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019)
- [Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/1908.08210/) (2019)
- [Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2005.03572/) (2020)
- [RelTR: Relation Transformer for Scene Graph Generation](https://mlsanity.com/paper/2201.11460/) (2022)
- [Scene Graph Generation: A Comprehensive Survey](https://mlsanity.com/paper/2201.00443/) (2022)
- [Personal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming](https://mlsanity.com/paper/2101.01060/) (2021)
- [Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images](https://mlsanity.com/paper/1909.04087/) (2019)
- [Applying deep learning to classify pornographic images and videos](https://mlsanity.com/paper/1511.08899/) (2015)
- [Pornographic Image Recognition via Weighted Multiple Instance Learning](https://mlsanity.com/paper/1902.03771/) (2019)
- [Privacy-sensitive Objects Pixelation for Live Video Streaming](https://mlsanity.com/paper/2101.00604/) (2021)

## Abstract (arXiv)

With the rise of social platforms, protecting privacy has become an important issue. Privacy object detection aims to accurately locate private objects in images. It is the foundation of safeguarding individuals' privacy rights and ensuring responsible data handling practices in the digital age. Since privacy of object is not shift-invariant, the essence of the privacy object detection task is inferring object privacy based on scene information. However, privacy object detection has long been studied as a subproblem of common object detection tasks. Therefore, existing methods suffer from serious deficiencies in accuracy, generalization, and interpretability. Moreover, creating large-scale privacy datasets is difficult due to legal constraints and existing privacy datasets lack label granularity. The granularity of existing privacy detection methods remains limited to the image level. To address the above two issues, we introduce two benchmark datasets for object-level privacy detection and propose SHAN, Scene Heterogeneous graph Attention Network, a model constructs a scene heterogeneous graph from an image and utilizes self-attention mechanisms for scene inference to obtain object privacy. Through experiments, we demonstrated that SHAN performs excellently in privacy object detection tasks, with all metrics surpassing those of the baseline model.