  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.
