# DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

arXiv: https://arxiv.org/abs/2203.09121 · HTML: https://mlsanity.com/paper/2203.09121/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Graph-Structured Representations for Visual Question Answering](https://mlsanity.com/paper/1609.05600/) (2016)
- [Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images](https://mlsanity.com/paper/1703.10660/) (2017)
- [Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition](https://mlsanity.com/paper/2012.02994/) (2020)
- [The More You Know: Using Knowledge Graphs for Image Classification](https://mlsanity.com/paper/1612.04844/) (2016)
- [Deep Reasoning with Knowledge Graph for Social Relationship Understanding](https://mlsanity.com/paper/1807.00504/) (2018)
- [Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective](https://mlsanity.com/paper/1703.09471/) (2017)
- [Faceless Person Recognition; Privacy Implications in Social Media](https://mlsanity.com/paper/1607.08438/) (2016)
- [Adversarial Scene Editing: Automatic Object Removal from Weak Supervision](https://mlsanity.com/paper/1806.01911/) (2018)
- [Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks](https://mlsanity.com/paper/1504.08289/) (2015)
- [Dual-Glance Model for Deciphering Social Relationships](https://mlsanity.com/paper/1708.00634/) (2017)
- [Face Detection using Deep Learning: An Improved Faster RCNN Approach](https://mlsanity.com/paper/1701.08289/) (2017)
- [Dynamic Deep Multi-modal Fusion for Image Privacy Prediction](https://mlsanity.com/paper/1902.10796/) (2019)

## Built on by (most cited first, 4 of 4)

- [Content-based Graph Privacy Advisor](https://mlsanity.com/paper/2210.11169/) (2022)
- [Image-guided topic modeling for interpretable privacy classification](https://mlsanity.com/paper/2409.18674/) (2024)
- [Learning Privacy from Visual Entities](https://mlsanity.com/paper/2503.12464/) (2025)
- [On the Robustness of Vision-Language Models in Zero-shot Privacy Classification](https://mlsanity.com/paper/2510.09253/) (2025)

## Abstract (arXiv)

The daily practice of sharing images on social media raises a severe issue about privacy leakage. To address the issue, privacy-leaking image detection is studied recently, with the goal to automatically identify images that may leak privacy. Recent advance on this task benefits from focusing on crucial objects via pretrained object detectors and modeling their correlation. However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors; 2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images. To overcome the limitations, we propose the Dynamic Region-Aware Graph Convolutional Network (DRAG) that dynamically finds out crucial regions including objects and other important elements, and models their correlation adaptively for each input image. To find out crucial regions, we cluster spatially-correlated feature channels into several region-aware feature maps. Further, we dynamically model the correlation with the self-attention mechanism and explore the interaction among the regions with a graph convolutional network. The DRAG achieved an accuracy of 87% on the largest dataset for privacy-leaking image detection, which is 10 percentage points higher than the state of the art. The further case study demonstrates that it found out crucial regions containing not only objects but other important elements like textures.